System, method and computer program for facet analysis

ABSTRACT

Automated facet analysis of input information selected from a domain of information in accordance with a source data structure is described. Facet analysis may proceed by discovering at least one of facets, facet attributes, and facet attribute hierarchies of the input information using pattern augmentation and statistical analyses to identify patterns of facet attribute relationships in the input information.

CROSS REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. patent application Ser. No.11/550,457, filed on Oct. 18, 2006 and entitled “System, Method andComputer Program for Facet Analysis, which application is a continuationin part of U.S. patent application Ser. No. 11/469,258, filed Aug. 31,2006 and entitled “Complex-Adaptive System For Providing A FacetedClassification”, which application is a continuation in part of U.S.patent application Ser. No. 11/392,937, filed Mar. 30, 2006, nowabandoned, and which application claimed the benefit of U.S. ProvisionalPatent Application 60/666,166, filed Mar. 30, 2005. Each of theabove-listed applications is incorporated by reference herein in itsentirety.

FIELD OF THE INVENTION

This invention relates to classification systems, specifically toautomated systems of facet analysis.

BACKGROUND OF THE INVENTION

Faceted classification is based on the principle that information has amulti-dimensional quality, and can be classified in many different ways.Subjects of an informational domain are subdivided into facets (or moresimply, categories) to represent this dimensionality. The attributes ofthe domain are related in facet hierarchies. The materials within thedomain are then identified and classified based on these attributes.

FIG. 1 illustrates the general approach of faceted classification in theprior art, as it applies (for example) to the classification of wine.

Faceted classification is known as an analytico-synthetic method, as itinvolves processes of both analysis and synthesis. To devise a schemefor faceted classification, information domains are analyzed todetermine their basic facets. The classification must then besynthesized (or built) by applying the attributes of these facets to thedomain based on constructive rules.

Faceted classification is a very labor-intensive and intellectuallychallenging endeavor. In facet analysis, structural patterns (such assemantic or syntactical structures) must be identified within thedomain. There are many different patterns that may identify facets andattributes within a domain. While people can be trained to identifythese patterns on small (local) data sets, the task becomesprohibitively difficult as the size of the domain increases.

To help address the complexity of the task, scholars have devised rulesand guidelines for faceted classification. Though technology has beenenlisted in the service of facet analysis, by and large, this technologyhas been applied within the historical methods and organizing principlesof traditional facet analysis theory. People remain key inputs and facetanalysis remains an overwhelming human activity.

Thus, there are many disadvantages with the current state of the art inautomated facet analysis. The input of human cognition is requisite, asthere are no universal patterns or heuristics for facet analysis thatwork across all information domains. Presently, only humans possess thefall breadth of pattern recognition skills.

Hybrid systems that involve humans at critical stages in the process,typically early on in the process, are often bottlenecked in theirclassification efforts. As such, the process remains slow and costly.Systems are needed that accept classification data from people in a moredecentralized, ad hoc manner that does not require centralized controland authority.

Humans are adept at assessing the relationships between informationalelements at a small scale, but fail to manage the complexity over anentire domain in the aggregate. Systems are needed that are able toaggregate small, localized human inputs across an entire domain ofinformation.

Hybrid systems that are based on existing universal schemes of facetedclassification rarely apply to the massive and rapidly evolving modernworld of information. There is a pressing need for custom-designedschemes, specialized to the needs of individual domains.

Since universal schemes cannot be applied universally, there is also aneed to connect different domains of information together. Systems offacet analysis are needed to provide for universal facets and attributesthat may be combined in novel ways to generate custom-designedclassification schemes. In other words, facet analysis may provide ameans for fundamentally connecting disparate domains together, withoutprescribing the use of universal classifications.

SUMMARY

Automated facet analysis of input information selected from a domain ofinformation in accordance with a source data structure is described.Facet analysis may proceed by discovering at least one of facets, facetattributes, and facet attribute hierarchies of the input informationusing pattern augmentation and statistical analyses to identify patternsof facet attribute relationships in the input information.

In a first aspect there is provided a method for performing facetanalysis of input information selected from a domain of information inaccordance with a source data structure. The method comprisesdiscovering at least one of facets, facet attributes, and facetattribute hierarchies of the input information using patternaugmentation and statistical analyses to identify patterns of facetattribute relationships in the input information.

In a second aspect, there is provided a computer system for performingfacet analysis of input information selected from a domain ofinformation in accordance with a source data structure. The computersystem is configured for discovering at least one of facets, facetattributes, and facet attribute hierarchies of the input informationusing pattern augmentation and statistical analyses to identify patternsof facet attribute relationships in the input information.

In a further aspect, there is provided a computer program productstoring instructions and data to configure a computer system forperforming facet analysis of input information selected from a domain ofinformation in accordance with a source data structure. The instructionsand data configuring the computer system for discovering at least one offacets, facet attributes, and facet attribute hierarchies of the inputinformation using pattern augmentation and statistical analyses toidentify patterns of facet attribute relationships in the inputinformation.

These and other aspects will be apparent to those of ordinary skill inthe art.

BRIEF DESCRIPTION OF THE DRAWINGS

The invention will be better understood with reference to the drawings,in which:

FIG. 1 is a schematic diagram illustrating a method of facetedclassification of the prior art;

FIG. 2 illustrates an overview of operations showing data structuretransformations to create a dimensional concept taxonomy for a domain;

FIG. 3 illustrates a knowledge representation model useful for theoperations of FIG. 2;

FIG. 4 illustrates the manner in which the operations generatedimensional concepts from elemental constructs;

FIG. 5 illustrates how the operations combine dimensional conceptrelationships to generate dimensional concept taxonomies;

FIG. 6 illustrates a system overview in accordance with a preferredembodiment to execute the operations of data structure transformation;

FIG. 7 illustrates faceted data structures used in the preferredembodiment, and the multi-tier architecture that supports thesestructures;

FIG. 8 illustrates in further detail an overview of the operations ofFIG. 2;

FIG. 9 illustrates a method of extracting input data;

FIG. 10 illustrates a method of source structure analytics;

FIG. 11 illustrates a process of extracting preliminary concept-keyworddefinitions;

FIG. 12 illustrates a method of extracting morphemes;

FIGS. 13-14 illustrate a process of calculating potential morphemerelationships from concept relationships;

FIGS. 15A-15B, 16 and 17 illustrate a process of assembling apolyhierarchy of morpheme relationships from the set of potentialmorpheme relationships;

FIGS. 18A, 18B and 19 illustrate the reordering of morphemepolyhierarchy into a strict hierarchy using a method of attribution;

FIGS. 20A and 20B illustrate sample fragments from a morpheme hierarchyand a keyword hierarchy;

FIG. 21 illustrates a method of preparing output data for use inconstructing the dimensional concept taxonomy;

FIGS. 22, 23 and 24 illustrate how faceted output data is used toconstruct a dimensional concept taxonomy;

FIG. 25 illustrates a dimensional concept taxonomy build for a localizeddomain set;

FIG. 26 illustrates a view of a dimensional concept taxonomy in abrowser-based user interface;

FIG. 27 illustrates an environment for user interactions in anoutliner-based user interface;

FIG. 28 illustrates a process of user interactions that edit contentcontainers within the dimensional concept taxonomy;

FIG. 29 illustrates a series of user interactions and feedback loops inthe complex-adaptive system;

FIG. 30 illustrates operations of personalization;

FIG. 31 illustrates operations of a machine-based complex-adaptivesystem;

FIG. 32 illustrates a computing environment and architecture componentsfor a system for executing the operations in accordance with anembodiment; and

FIG. 33 illustrates a simplified data schema in the preferredembodiment.

DETAILED DESCRIPTION

1.1 System Operation

1.1.1 Overview

FIGS. 2-8 provide an overview of operations and a system forconstructing and managing dimensional information structures such as tocreate a dimensional concept taxonomy for a domain. In particular, FIGS.2-8 show a knowledge representation model useful for such operations aswell as certain dimensional data structures and constructs. Also shownare methods of data structure transformation including acomplex-adaptive system and an enhanced method of facetedclassification.

1.1.1.1 Overview of Operations

Analysis and Compression

FIG. 2 illustrates operations to construct a dimensional concepttaxonomy 210 for a domain 200 comprising a corpus of information that isthe subject matter of a classification. Domain 200 may be represented bya source data structure 202 comprised of a source structure schema and aset of source data entities derived from the domain 200 for inputting toa process of analysis and compression 204. The process of analysis andcompression 204 derives a morpheme lexicon 206 that is an elemental datastructure comprised of a set of elemental constructs to provide a basisfor the new faceted classification scheme.

The information in domain 200 may relate to virtual or physical objects,processes, and relationships between such information. Preferably, theoperations described herein are directed to the classification ofcontent residing within Web pages. Alternate embodiments of domain 200may include document repositories, recommendation systems for music,software code repositories, models of workflow and business processes,etc.

The elemental constructs within the morpheme lexicon 206 are a minimumset of fundamental building blocks of information and informationrelationships which in the aggregate provide the information-carryingcapacity with which to classify the source data structure 202.

Synthesis and Expansion

Morpheme lexicon 206 is the input to a method of synthesis and expansion208. The synthesis and expansion operations transform the source datastructure 202 into a third data structure, referred to herein as thedimensional concept taxonomy 210. The term “taxonomy” refers to astructure that organizes categories into a hierarchical tree andassociates categories with relevant objects such as documents or otherdigital content. The dimensional concept taxonomy 210 categorizes sourcedata entities from domain 200 in a complex dimensional structure derivedfrom the source data structure 202. As a result, source data entities(objects) may be related across many different organizing bases,allowing them to be found from many different perspectives.

In the illustration of FIG. 2, and in all illustrations containedherein, triangle shapes are used to represent relatively simple datastructures and pyramid shapes are used to represent relatively complexdata structures embodying higher dimensionality. Varying sizes of thetriangles and pyramids represent transformations of compression andexpansion, but in no way indicate or limit the precise scale of thecompression or transformation.

Complex-Adaptive System

Preferably, classification systems and operations should adapt to changein dynamic environments. In the preferred embodiment, this requirementis met through a complex-adaptive system 212. Feedback loops areestablished through user interactions with the dimensional concepttaxonomy 210 back to the source data structure 202. The processes oftransformation (204 and 208) repeat and the resultant structures 206 and210 are refined over time.

In the preferred embodiment, the complex-adaptive system 212 manages theinteractions of end-users that use the output structures (i.e.dimensional concept taxonomies 210) to harness the power of humancognition in the classification process.

The operations described herein seek to transform relatively simplysource data structures to more complex dimensional structures in orderthat the source data objects may be organized and accessed in a varietyof ways. Many types of information systems may be enhanced by extendingthe dimensionality and complexity of their underlying data structures.Just as higher resolution increases the quality of an image, higherdimensionality increases the resolution and specificity of the datastructures. This increased dimensionality in turn enhances the utilityof the data structures. The enhanced utility is realized throughimproved and more flexible content discovery (e.g. through searching),improvements in information retrieval, and content aggregation.

Since the transformation is accomplished through a complex system, theincrease in dimensionality is not necessarily linear or predictable. Thetransformation is also dependent in part on the amount of informationcontained in the source data structure.

1.1.1.2 Dimensional Knowledge Representation Model

FIG. 3 illustrates an embodiment of a knowledge representation modelincluding knowledge representation entities, relationships, and methodof transformation that may be used in the operations of FIG. 2. Furtherspecifics of the knowledge representation model and its methods oftransformation are described in the descriptions that follow withreference to FIGS. 3-8.

The knowledge representation entities in the preferred embodiment of theinvention are a set of content nodes 302, a set of content containers304, a set of concepts 306 (to simplify the illustration, only oneconcept is presented in FIG. 3), a set of keywords 308, and a set ofmorphemes 310.

The objects of the domain to be classified are known as content nodes302. Content nodes are comprised of any objects that are amenable toclassification. For example, content nodes 302 may be a file, adocument, a chunk of document (like an annotation), an image, or astored string of characters. Content nodes 302 may reference physicalobjects or virtual objects.

Content nodes 302 are contained in a set of content containers 304.Preferably, the content containers 304 provide addressable (orlocatable) information through which content nodes 302 can be retrieved.For example, the content container 304 of a Web page, addressablethrough a URL, may contain many content nodes 302 in the form of textand images. Content containers 304 contain one or more content nodes302.

Concepts 306 are associated with content nodes 302 to abstract somemeaning (such as the description, purpose, usage, or intent of thecontent node 302). Individual content nodes 302 may be assigned manyconcepts 306; individual concepts 306 may be shared across many contentnodes 302.

Concepts 306 are defined in terms of compound levels of abstractionthrough their relationships to other entities and structurally in termsof other, more fundamental knowledge representation entities (e.g.keywords 308 and morphemes 310). Such a structure is known herein as aconcept definition.

Morphemes 310 represent the minimal meaningful knowledge representationentities that present across all domains known by the system (i.e. thathave been analyzed to construct the morpheme lexicon 206). A singlemorpheme 310 may be associated with many keywords 308; a single keyword308 may be comprised of one or more morphemes 310.

Further there is a distinction between the meaning of the term“morphemes” in the context of this specification and its traditionaldefinition in the field of linguistics. In linguistics, morphemes arethe “minimal meaningful units of a language”. In the context of thisspecification, morphemes refer to the “minimal meaningful knowledgerepresentation entities that present across all domains known by thesystem.”

Keywords 308 comprise sets (or groups) of morphemes 310. A singlekeyword 308 may be associated with many concepts 306; a single concept306 may be comprised of one or more keywords 308. Keywords 308 thusrepresent an additional tier of data structure between concepts 306 andmorphemes 310. They facilitate “atomic concepts” as the lowest level ofknowledge representation that would be recognizable to users.

Since concepts 306 are abstracted from the content nodes 302, a conceptsignature 305 is used to identify concepts 306 within concept nodes 302.Concept signatures 305 are those features of a content node 302 that arerepresentative of organizing themes that exist in the content.

In the preferred embodiment, as with the elemental constructs, contentnodes 302 tend towards their most irreducible form. Preferably, contentcontainers 304 are reduced to as many content nodes 302 as is practical.When combined with the extremely fine mode of classification in thepresent invention, these elemental content nodes 302 extend the optionsfor content aggregation and filtering. Content nodes 302 may thus bereorganized and recombined along any dimension in the dimensionalconcept taxonomy.

A special category of content nodes 302, namely labels (often called“terms” in the art of classification) are joined to each knowledgerepresentation entity. As with content nodes 302, labels are abstractedfrom the respective entities they describe in the knowledgerepresentation model. Thus in FIG. 3, the following types of labels areidentified: a content container label 304 a to describe the contentcontainer 304; a content node label 302 a to describe the content node302; a concept label 306 a to describe the concept 306; a set of keywordlabels 308 a to describe the set of keywords 308; a set of morphemelabels 310 a to describe the set of morphemes 310.

Labels provide knowledge representation entities that are discernable tohumans. In the preferred embodiment, each label is derived from theunique vocabulary of the source domain. In other words, the labelsassigned to each data element are drawn from the language and termspresented in the domain.

Concept, keyword, and morpheme extraction are described below andillustrated in FIGS. 11-12. Concept signatures and content node andlabel extraction are discussed in greater detail below with reference toinput data extraction (FIG. 9).

The preferred embodiment uses a multi-tier knowledge representationmodel. This differentiates it from the two-tier model of concepts-atomicconcepts in traditional faceted classification, as illustrated in FIG. 1(Prior Art).

Though certain aspects of the operations and system are described withreference to the preferred knowledge representation model, those ofordinary skill in the art will appreciate that other models may used,adapting the operations and system accordingly. For example, conceptsmay be combined together to create higher-order knowledge representationentities (such as “meme”, as a collection of concepts to comprise anidea). The structure of the representation model may also be contracted.For example, the keyword abstraction layer may be removed such thatconcepts are defined only in relation to morphemes 310.

1.1.1.3 Dimensional Classification Synthesis

FIGS. 4-5 illustrate the methods through which the elemental constructsare derived and synthesized to create complex dimensional structures.

Dimensional Concept Synthesis

In FIG. 4, a sample of morphemes 310 are presented. Morphemes 310 areamong the elemental constructs derived from the source data. The otherset of elemental constructs are comprised of a set of morphemerelationships. Just as morphemes represent the elemental building blocksof concept definitions and are derived from concepts, morphemerelationships represent the elemental building blocks of therelationships between concepts and are derived from such conceptrelationships. Morpheme relationships are discussed in greater detailbelow, illustrated in FIGS. 13-14.

Morphemes 310 that comprise the concept definitions are related in amorpheme hierarchy 402. The morpheme hierarchy 402 is an aggregate setof all the morpheme relationships known in the morpheme lexicon 206,pruned of redundant morpheme relationships. Morpheme relationships areconsidered redundant if they can be logically constructed using sets ofother morpheme relationships (i.e. through indirect relationships).

With reference to FIG. 4, individual morphemes 310 a and 310 b may begrouped in keywords to define a specific concept 306 b. Note that thesemorphemes 310 a and 310 b are thus associated with a concept 306 b (viakeyword groupings) and with other morphemes 310 in the morphemehierarchy 402.

Through these interconnections, the morpheme hierarchy 402 can be usedto create a new and expansive set of concept relationships.Specifically, any two concepts 306 that contain morphemes 310 that arerelated through morpheme relationships may themselves be relatedconcepts.

Co-occurrences of morphemes within concept definitions may be used asthe basis for creating hierarchies of concept relationships. Eachintersecting line 406 a and 406 at concept 306 a (FIG. 4) represents adimensional axis connecting concept 306 a to other related concepts (notshown). The set of dimensional axes, each representing a separatehierarchy of concept relationships filtered by a set of morphemes (orfacet attributes) that define the axis, is the structural foundation ofa complex dimensional structure. A simplified overview of theconstruction method continues in FIG. 5.

Dimensional Concept Taxonomy

FIG. 5 illustrates the construction of the complex dimensional structurefor defining dimensional concept taxonomy 210 based on the intersectionof dimensional axes.

A set of four concepts 306 c, 306 d, 306 e, and 306 f are illustratedwith concepts 306 c, 306 d, and 306 e defined by morphemes 310 c, 310 d,and 310 e, respectively and concept 306 f defined by the set ofmorphemes 310 c, 310 d, and 310 e. By virtue of the intersections of themorphemes 310 c, 310 d, and 310 e, the concepts 306 c, 306 d, 306 e, and306 f share concept relationships. Synthesis operations (describedbelow) create dimensional axes 406 c, 406 d, and 406 e as distincthierarchies of concept relationships based on the morphemes 310 c, 310d, and 310 e in the concept definitions.

This operation of synthesizing dimensional concept relationships may beprocessed to all or a portion of content nodes 302 in the domain 200(scope-limited processing operations are described below, illustrated inFIGS. 24-25). Content nodes 302 may thus be categorized into acompletely reengineered complex dimensional structure, as thedimensional concept taxonomy 210.

1.1.1.4 Dimensional Transformation Processes

FIG. 6 illustrates a system overview in accordance with a preferredembodiment to execute the operations of data structure transformationdescribed above and further herein below.

The three broad processes of transformation introduced above may berestated in more detailed terms, as they present in the preferredembodiment: 1) the analysis and compression of domain 200 to discoverfacets of its structure, as defined in terms of the elemental constructsin the complex dimensional structure; 2) the synthesis and expansion ofthe complex dimensional structure of the domain into the dimensionalconcept taxonomy 210, provided through an enhanced method of facetedclassification; and 3) the management of user interactions within thedimensional concept taxonomy 210, through a faceted navigation andediting environment, to enable the complex-adaptive system that refinesthe structures (e.g. 206 and 210) over time.

Analysis of Elemental Constructs

In the preferred embodiment, a distributed computing environment 600 isshown schematically. One computing system 601 operates as atransformation engine 602 for data structures. The transformation enginetakes as its inputs the source data structures 202 from one or moredomains 200. The transformation engine 602 is comprised of an analysisengine 204 a, a morpheme lexicon 206, and a build engine 208 a. Thesesystem components provide the functionality of analysis and synthesisintroduced above and illustrated in FIG. 2.

In the preferred embodiment, the complex dimensional structure isencoded into XML files 604 that may be distributed via web services (orAPI or other distribution channels) over the Internet 606 to one or moresecond computing systems (e.g. 603). Through this and/or other modes ofdistribution and decentralization, a wide range of developers andpublishers can use the transformation engine 602 to create complexdimensional structures. Applications include web sites, knowledge bases,e-commerce stores, search services, client software, managementinformation systems, analytics, etc.

Synthesis Through Enhanced Faceted Classification

The complex dimensional structures embodied in the XML files 604 areavailable as the bases for reorganizing the content of domains. In thepreferred embodiment, an enhanced method of faceted classification isused to reorganize the materials in the domain, deriving the dimensionalconcept taxonomy 210 at a second computing system 603 using the complexdimensional structures embodied in the XML files 604. Typically, secondcomputing systems like system 603 are maintained by domain owners thatare also responsible for the domain to be reorganized by the dimensionalconcept taxonomy 210. Detailed information on the multi-tier datastructures used by the system is provided below, illustrated in FIG. 7.

In the preferred embodiment of the system 603, there is provided apresentation layer 608 or graphical user interface (GUI) for thedimensional concept taxonomy 210. Client-side tools 610 such asbrowsers, web-based forms, and software components allow domainend-users and domain owners/administrators to interact with thedimensional concept taxonomy 210.

Complex-Adaptive Processing Via User Interactions

The dimensional concept taxonomies 210 may be tailored and demarcated byeach individual end-user and domain owner. These user interactions maybe harnessed by second computing systems (e.g. 603) to provide humancognition and additional processing resources to the classificationsystem.

Dimensional taxonomy information that embody the user interactions forexample, encoded in XML 212 a, are returned to the transformation engine602 such as by distributing via web services or other means. This allowsthe data structures (e.g. 206 and 210) to evolve and improve over time.

The feedback loops from second systems 603 to the transformation engine602 establish the complex-adaptive system of processing. While end-usersand domain owners interact at a high level of abstraction through thedimensional concept taxonomy 210, the user interactions are translatedto the elemental constructs (e.g. morphemes and morpheme relationships)that underlie the dimensional concept taxonomy information. By couplingthe end-user and domain owner interactions to the elemental constructsand feeding them back to the transformation engine 602, the system isable to evaluate the interactions in the aggregate.

Using this mechanism, ambiguity and conflict that historically arise incollaborative classification may be removed. Thus, this approach tocollaborative classification seeks to avoid the personal andcollaborative negotiations on the concept level that may arise withother such systems.

User interactions also extend the source data 202 available by allowingusers to contribute content nodes 302 and classification data(dimensional concept taxonomy information) through their interactions,enhancing the overall quality of the classifications and increasing theprocessing resources available.

1.1.1.5 Overview of Data Structure Transformations

FIG. 7 highlights the means by which the elemental constructs harvestedfrom each source data structure 202 are compounded through successivelevels of abstraction and dimensionality to create the dimensionalconcept taxonomies 210 for each domain 200. It also illustrates thedelineations between the private data (708, 710 and 302) embodied ineach domain 200 and the shared elemental constructs 206 that the systemuses to inform the classification schemes generated for each domain.

Elemental Constructs

The elemental constructs of morphemes 310 and morpheme relationships arestored in the morpheme lexicon 206 as centralized data. The centralizeddata is centralized across the distributed computing environment 600(e.g. via transformation engine system 601) and made available to alldomain owners and end-users to aid in the classification of domains.Since the centralized data is elemental (morphemic) and disassociatedfrom the context of any specific and private knowledge represented byconcepts 306 and concept relationships, it can be shared among secondcomputing systems 603. System 601 need not permanently store the uniqueexpression and combination of these elemental constructs that comprisesthe unique information contained in each domain.

The morpheme lexicon 206 stores the attributes of each morpheme 310 in aset of tables of morpheme attributes 702. The morpheme attributes 702reference structural parameters and statistical data that are used byanalytical processes of the transformation engine 602 (as describedfurther below). The morpheme relationships are ordered in the aggregateinto the morpheme hierarchy 402.

Dimensional Faceted Output Data

A domain data store 706 stores the domain-specific data (complexdimensional structures 210 a), preferably in XML form, derived by thetransformation engine system 601 from the source data structure 202 andusing the morpheme lexicon 206.

The XML-based complex dimensional structures 210 a in each domain datastore 706 are comprised of a domain-specific keyword hierarchy 710, aset of content nodes 302, and a set of concept definitions 708. Thekeyword hierarchy 710 is comprised of a hierarchical set of keywordrelationships. Preferably, the XML output is itself encoded as faceteddata. The faceted data represents the dimensionality of the source datastructure 202 as facets of its structure, and the content nodes 302 ofthe source data structure 202 in terms of attributes of the facets. Thisapproach allows domain-specific resources (e.g. system 603) to processthe complex dimensional structures 210 a into higher levels ofabstraction such as dimensional concept taxonomy 210.

The complex dimensional structure 210 a is used as an organizing basisto manage the relationships between content nodes 302. A new set oforganizing principles is then applied to the elemental constructs forclassification. The organizing principles comprise an enhanced method offaceted classification as detailed below, illustrated in FIGS. 22-24.

Preferably, the enhanced method of faceted classification is applied tothe complex dimensional structures 210 a. Other simpler classificationmethods may also be applied and other data structures (whether simple orcomplex) may be created from the complex dimensional structures 210 a asdesired. In the preferred embodiment, an output schema that explicitlyrepresents faceted classifications is used. Other output schema may beused. The faceted classifications produced for each domain may berepresented using a variety of data models. The methods ofclassification available are closely associated with the types of datastructures being classified. Therefore, these alternate embodiments forclassification are directly linked to the alternate embodiments ofdimensionality, discussed above.

Shared Versus Private Data

An advantage of the dimensional knowledge representation model is theclear separation of private domain data and shared data used by thesystem to process domains into complex dimensional structures 210 a.Data separation facilitates hosted processing models, such as an ASPmodel, whereby a third-party offers transformation engine services todomain owners. A domain owner's domain-specific data may be hosted bythe ASP securely as it is separable from the shared data (i.e. morphemelexicon 206) and the private data of other domain owners. Alternately,the domain-specific data may be hosted by the domain owners, physicallyremoved from the shared data. Domain owners can build on the sharedknowledge (e.g. the morpheme lexicon) of the entire community of users,without having to compromise their unique knowledge.

Data entities (e.g. 708, 710) contained in the domain data store 706include references to the elemental constructs that are stored in themorpheme lexicon 206. In this way, the dimensional concept taxonomy 210for each domain 200 can be re-analyzed subsequent to its creation, toaccommodate changes. Preferably, when domain owners want to update theirclassifications, domain-specific data is reloaded into the analysisengine 204 a for processing. A domain 200 may be analyzed in real-time(for example, through end-user interactions via XML 212 a) or through(queued) periodic updates.

1.1.1.6 Overview of System Transformation Methods

FIG. 8 illustrates a broad overview of a preferred embodiment of thetransformation operations 800 introduced in FIG. 2.

Input Data Extraction

Operations 800 begin with the manual identification by domain owners ofthe domain 200 to be classified. Preferably, source data structure 202is defined from a domain training set 802. The training set 802 may be arepresentative subset of the larger domain 200 and may be used as asurrogate. That is, the training set may comprise a source datastructure 202 for the whole domain 200 or a representative part thereof.Training sets are well known in the art.

A set of input data is extracted 804 from the domain training set 802.The input data is analyzed to discover and extract the elementalconstructs. (This process is discussed in greater detail below,illustrated in FIG. 9.)

Domain Facet Analysis and Data Compression

In the present embodiment, the analysis engine 204 a introduced aboveand described in FIG. 6 is bounded by the methods 806 to 814, asindicated by the bracket in FIG. 8. The input data is analyzed andprocessed 806 to provide a set of source structure analytics. The sourcestructure analytics provide information about the structuralcharacteristics of the source data structure 202. (This process isdiscussed in greater detail below, illustrated in FIG. 10.)

A set of preliminary concept definitions are generated 808. (Thisprocess is discussed in greater detail below, illustrated in FIG. 11.)The preliminary concept definitions are represented structurally as setsof keywords 308.

Morphemes 310 are extracted 810 from the keywords 308 in the preliminaryconcept definitions, thus extending the structure of the conceptdefinitions to another level of abstraction. (This process is discussedin greater detail below, illustrated in FIG. 12.)

To begin the process of constructing the morpheme hierarchy 402, a setof potential morpheme relationships is calculated 812. The potentialmorpheme relationships are derived from an analysis of the conceptrelationships in the input data. Morpheme structure analytics areapplied to the potential morpheme relationships to identify those thatwill be used to create the morpheme hierarchy.

The morpheme relationships selected for inclusion in the morphemehierarchy are assembled 814 to form the morpheme hierarchy 402. (Thisprocess is discussed in greater detail below, illustrated in FIGS.13-19.)

Dimensional Structure Synthesis and Data Expansion

In the present embodiment, build engine 208 a introduced above anddescribed in FIG. 6 is bounded by the methods 818 to 820, as indicatedby the bracket in FIG. 8. The enhanced method of faceted classificationis used to synthesize the complex dimensional structure 210 a and thedimensional concept taxonomy 210. (This process is discussed in greaterdetail below, illustrated in FIGS. 22-24.)

Output data 210 a for the new dimensional structure is prepared 818. Theoutput data is the structural representation of the classificationscheme for the domain. It is used as faceted data to create thedimensional concept taxonomy 210. As described above, the output datacomprises the concept definitions 708 that are associated with thecontent nodes 302 and the keyword hierarchy 710. Specifically, thefaceted data is comprised of the keywords 308 in the concept definitionsand the structure of the keyword hierarchy 710 where the keywords 308are defined in terms of the morphemes 310 of the morpheme lexicon 206.(This process is discussed in greater detail below, illustrated in FIG.21.)

A set of dimensional concept relationships (that in the aggregate formpolyhierarchies) are constructed 820. The dimensional conceptrelationships represent the concept relationships in the dimensionalconcept taxonomy 210. The dimensional concept relationships arecalculated based on the organizing principles of the enhanced method offaceted classification. The dimensional concept relationships are mergedand, within the categorization of concepts 306 (as encoded in conceptdefinitions), form the dimensional concept taxonomy 210. (This processis discussed in greater detail below, illustrated in FIGS. 22-24.)

Complex-Adaptive System and User Interactions

In the present embodiment, the operations of the complex-adaptive system212 introduced above and described in FIG. 2 are bounded by the methods212 a, 212 b, and 804, in association with the concept taxonomy 210, asindicated by the bracket in FIG. 8.

As discussed, the dimensional concept taxonomy 210 may be expressed tousers through the presentation layer 608. In the preferred embodiment,the presentation layer 608 is a web site. (The presentation layer isdiscussed in greater detail below, illustrated in FIGS. 25-28.) Via thepresentation layer 608, the content nodes 302 in the domain 200 arepresented as categorized within the concept definitions that areassociated with each content node 302.

This presentation layer 608 provides the environment for collecting aset of user interactions 212 a as dimensional concept taxonomyinformation. The user interactions 212 a are comprised of various waysin which end-users and domain owners may interact with the dimensionalconcept taxonomy 210. The user interactions 212 a are coupled to theanalysis engine via a feedback loop through step 804 to extract inputdata to enable the complex-adaptive system. (This process is discussedin greater detail below, illustrated in FIG. 29.)

In one embodiment, the user interactions 212 a returned in the explicitfeedback loop may be queued for processing as resources becomeavailable. Accordingly, an implicit feedback loop is preferablyprovided. The implicit feedback loop is based on a subset of theorganizing principles of the enhanced method of faceted classificationto calculate implicit concept relationships 212 b. Through the implicitfeedback loop, the user interactions 212 a with the dimensional concepttaxonomy 210 are processed in near real-time.

Through the complex-adaptive system 212, the classification scheme thatderives the dimensional concept taxonomy 210 is continually honed andexpanded.

1.1.2 Domain Facet Analysis and Data Extraction

1.1.2.1 Extract Input Data

FIG. 9 illustrates operations 900 comprising operations to extract theinput data 804 and certain preliminary steps thereto as discussedbriefly with reference to FIG. 8.

Identify Structural Markers

Structural markers are identified 902 within the training set 802 toindicate where input data may be extracted from the training set. Thestructural markers comprise a source structure schema. The structuralmarkers present in content containers 304 and may include, but are notlimited to, the title of the document, descriptive meta tags associatedwith content, hyperlinks, relationships between tables in a database, orthe prevalence of keywords 308 that exist in content containers. Themarkers may be identified by domain owners or others.

Operations 900 may be configured with default structural markers thatapply across domains. For example, the URLs of Web pages are a commonstructural marker for content nodes 302. As such, the operations 902 canbe configured with a multitude of default structural patterns that wouldapply in the absence of any explicit references in those areas in thesource structure schema.

The structural markers may be located in the input data explicitly, ormay be located as surrogates for the input data. For example,relationships between content nodes 302 may be used as the surrogatestructural marker for concept relationships.

In the preferred embodiment, the structural markers may be combined togenerate logical inferences about the source structure schema. Ifconcept relationships are not explicit in the source structure schema,they may be inferred from structural markers such as concept signaturesassociated with content nodes 302, and a set of content noderelationships. For example, a concept signature may be a title in adocument mapped as a surrogate for a concept to be defined as describedfurther. Content node relationships may be derived from the structurallinkages between content nodes 302, such as the hyperlinks that connectWeb pages.

The connection of concept signatures to content nodes 302, and theconnection of content nodes 302 to other content nodes 302, infersconcept relationships among the intersecting concepts. Theserelationships form additional (explicit) input data.

There are many different ways to identify structural markers as known tothose of ordinary skill in the art.

Map Source Structure Schema to System Input Schema

The source structure schema is mapped to an input schema 904. In thepreferred embodiment, the input schema is comprised of a set of conceptsignatures 906, a set of concept relationships 908, and a set of conceptnodes 302.

This schema design is representative of the transformation processes andis not intended to be limiting. The input operations do not requiresource input data across every data element in the system input schema,so as to accommodate very simple structures.

The system input schema may also be extended to map to every element ina system data transformation schema. The system data transformationschema corresponds to every data entity that presents in thetransformation processes. That is, the system input schema may beextended to map to every data entity in the system. In other words, thesource structure schema may be comprised of a subset of the system inputschema.

In addition, domain owners may map source data schema from very complexstructures. As an example, the tables and attributes of a relationaldatabase may be modeled as facet hierarchies at various levels ofabstraction and mapped to the multi-tier structure of the system datatransformation schema.

Again, operations of the analysis engine 204 a and build engine 208 aprovide a data structure transformation engine, and significant newutility is achieved in transforming one type of complex data structure(such as those modeled in relational databases) to another type ofcomplex data structure (the complex dimensional structures producedthrough the methods and systems described herein). Product catalogsprovide an example of complex data structures that benefit from thistype of complex-to-complex data structure transformation. Moreinformation on an example data transformation schema is provided below,illustrated in FIG. 32.

Extract Input Data

An input data map may be applied against the training set to map itssource structure schema to the input schema, extracting the input data804. The preferred embodiment uses XSLT to encode the data map, which isused to extract the data from source XML files, as is known in the art

The extraction methodology varies with many factors, including theparameters of the source structure schema and the location of thestructural markers. For example, if the concept signature is precise—aswith a document title, a keyword-based meta-tag, or a database keyfield—then the signature may be used directly to represent the conceptlabel. For more complex signatures—such as the prevalence of keywords inthe document itself—common text mining methodologies may be used. Asimple methodology bases keyword extraction on a simple count of themost prevalent keywords in the documents.

Once extracted, the input data may be stored in one or more storagemeans coupled to the analysis engine 204 a. For convenience, the figuresand descriptions contained herein reference a data store 910 as thestorage means but other stores may be used. For example, a domain datastore 706 may be used particularly if the computing environment is ahosted environment.

The system input data are split into their constituent sets and passedto subsequent processes in the transformation engine:

Concept relationships are the inputs for the source structure analyticsA, described below and illustrated in FIG. 10.

Concept signatures are processed to extract preliminary conceptdefinitions B, described below and illustrated in FIG. 11.

Content nodes are processed as system output data C, described below andillustrated in FIG. 21.

The extraction of input data from source data structures, as describedabove, is one of many embodiments that may be employed for extractinginput data. The other primary input channel to the analysis engine 204 ais the feedback loops that comprise the complex-adaptive system in thepreferred embodiment. As such, user interactions 212 a are returned O toprovide further input data. The details of this channel of input dataand the feedback loops that comprise the complex-adaptive system aredescribed below, illustrated in FIG. 29.

1.1.2.2 Processing of the Source Data Structure

FIG. 10 illustrates the processing of the source data structure toextract source structure analytics. The source structure analyticsprovide data relating to a topology of the source data structure. Thetopology of the source data refers to a set of technical characteristicsof the source data structure that describe its shape (characteristicssuch as the number of nodes contained in the structure, and thedispersal patterns of the relationships between nodes in the source datastructure).

A primary objective of this analytical method is to measure the degreeto which concepts 306 are general or specific (in relation to otherconcepts 306 in the training set 802). Herein, the measure of therelative generality or specificity of the concepts is referred to as the“generality”. The source data characteristics analyzed in the preferredembodiment are described below. Specifics on the analytics and thecharacteristics will vary with the source data structures.

Concept relationships 908 are assembled for analysis. Circularrelationships 1002 among the concepts 306 are identified (indicating thepresence of non-hierarchical relationships) and resolved.

All concept relationships that are identified by the system asnon-hierarchical are pruned from the set 1004. The pruned conceptrelationships are not involved in the subsequent processing, but may bemade available for processing based on different transformation rules.

The concept relationships that were not pruned are processed ashierarchical relationships. The system assembles these conceptrelationships 1006 into an input concept hierarchy 1008 of allhierarchical concept relationships ordered into extended sets ofindirect relationships. Assembling the input concept hierarchy 1008involves ordering the nodes in the aggregate and removing any redundantrelationships that may be inferred from other sets of relationships. Theinput concept hierarchy 1008 may comprise a polyhierarchy structurewhere entities may have more than one direct parent.

Once assembled, the input concept hierarchy 1008 comprises the structurefor measuring the generality of the concepts 306 in the conceptrelationship set, as described in the steps below and is useful forother methods in the transformation process. The concept relationshipsin the input concept hierarchy 1008 are used to calculate potentialmorpheme relationships D, as described below and illustrated in FIGS.13-14. The concept relationships in the input concept hierarchy are alsoused to process the output data for the system E, as described below andillustrated in FIG. 21.

The analysis of the input concept hierarchy proceeds to the measure ofthe generality of each concept 1010. Again, generality refers to howgeneral or specific any given node is relative to the other nodes in thehierarchy 1008. Each concept 306 is assessed a generality measurementbased on its location in the input concept hierarchy 1008.

Calculations are made of a weighted average degree of separation foreach concept 308 from each root in the tree that intersects with theconcept 306. The weighted average degree of separation refers to thedistance of each concept 306 from the concepts 306 at the root nodes.Concepts 306 that are unambiguously root nodes are assigned a generalitymeasure of one. The generality measurement increases for more specificconcepts 306, reflecting their increased degree of separation from themost general concepts 306 that reside at the root nodes. Those skilledin the art will appreciate that many other measures of generality arepossible.

The generality measurements for each concept 306 are stored in a conceptgenerality index 1012 (e.g. in data store 910). The concept generalityindex 1012 is used to infer a set of generality measurements for themorphemes F, as described below and illustrated in FIGS. 16-17.

The methods described in the preferred embodiment apply tohierarchical-type relationships, also known as parent-childrelationships. Parent-child relationships encompass a great deal ofdiversity in the types of relationships they can support. Examplesinclude: whole-part, genus-species, type-instance, and class-subclass.In other words, by supporting hierarchical type relationships, thepresent invention applies to a huge expanse of classification tasks.

1.1.2.3 Process Preliminary Concept Definitions

FIG. 11 illustrates a method of keyword extraction to generate thepreliminary concept definitions. A primary objective of this process isto generate a structural definition for the concepts 306 in terms ofkeywords 308. At this stage in the preferred embodiment, the conceptdefinitions are described as “preliminary” because they will be subjectto revision in later stages.

Those of ordinary skill in the art will appreciate that there are manymethods and technologies that may be directed to the goal of extractingkeywords 308 as structural representations of concepts 306.

In the preferred embodiment, the level of abstraction applied to keywordextraction is limited. These limits are designed to derive keywords withthe following qualities: Keywords are defined using (extracted based on)atomic concepts (where concepts present in other areas of the trainingset) and in response to the independence of words within directrelationship sets.

Concept signatures 906 and concept relationships 908 are gathered foranalysis. In the preferred embodiment, this process is based on theextraction of textual entities. As such, in the description thatfollows, the concept signatures 906 are assumed to map directly to theconcept labels that are assigned to concepts 306.

As labels are identified in the concept signatures 906, a relevantportion of the text string is extracted and used as the concept label306 a. In subsequent methods, as keywords 308 and morphemes 310 areidentified in concepts 306, labels for keywords 308 a and morphemes 310a are extracted from the relevant portions of the concept label 306 a.

These domain-specific labels are eventually written to the output data.If the operations 800 are transforming a data structure that has beenpreviously analyzed and classified, the entity labels are availabledirectly in the source data structure. More details on this are providedin the description of the output data, below.

Note that this juncture between concept signature and concept labelextraction represents an integration point for a wide variety of entityextraction tools, directed at many types of content nodes 302, such asimages, multimedia, and the classification of physical objects.

A series of keyword delineators are identified in the concept labels.Preliminary keyword ranges 1102 are parsed from the concept labels 306 abased on common structural delineators of keywords 308 (such asparentheses, quotes, and commas). Whole words are then parsed from thepreliminary keyword ranges 1104, again using common word delineators(such as spaces and grammatical symbols). These pattern-based approachesto textual entity parsing are well known in the art.

The parsed words from the preliminary keyword ranges 1102 comprise oneset of inputs for the next stage in the keyword extraction process. Theother set of inputs is a direct concept relationship set 1106. Thedirect concept relationship set 1106 is derived from the set of conceptrelationships 908. The direct concept relationship set 1106 is comprisedof all direct relationships (all direct parents and all direct children)for each concept 306.

These inputs are used to examine the independence of words in thepreliminary keyword ranges 1108. Single word independence within directrelationship sets 1106 comprises delineators for keywords 308. After thekeyword ranges have been delineated, checks are performed to ensure thatall portions of the derived keywords 308 are valid. Specifically, allsections of the concept label 306 a that are delineated as keywords 308must pass the word independence test.

In the preferred embodiment, the check for word independence is based ona method of word stem (or word root) matching, hereafter referred to as“stemming”. There are many methods of stemming, well known in the art.As described in the methods of morpheme extraction below, illustrated inFIG. 12, stemming provides an extremely fine basis for classification.

Based on the independence of words in the preliminary keyword ranges, anadditional set of potential keyword delineators 1110 are identified. Insimplified terms, if a word presents in one concept label 306 a withother words, and in a related concept label 306 a absent those samewords, than that word may delineate a keyword.

However, before the concept labels 306 a are parsed to keyword labels308 a on the basis of these keyword delineators, the candidate keywordlabels are validated 1112. All candidate keyword labels must pass theword independence test described above. This check prevents the keywordextraction process from fragmenting concepts 306 beyond the target levelof abstraction, namely atomic concepts.

Once a preliminary set of keyword labels is generated, the systemexamines all preliminary keyword labels in the aggregate. The intenthere is to identify compound keywords 1114. Compound keywords present asmore than one valid keyword label within a single concept label 306 a.This test is based directly on the objective of atomic keywords as thescope of the concept-keyword abstraction.

In the preferred embodiment, recursion is used to exhaustively split theset of compound keywords into the most elemental set of keywords 308that is supported by the training set 802.

If compound keywords remain in the evolving set of keyword labels, anadditional set of potential keyword delineators 1110 is generated, wherethe matching atomic keywords are used to locate the delineators. Again,the delineated keyword ranges are checked as valid keywords, keywordsare extracted, and the process repeats until no more atomic keywords canbe found.

A final method round of consolidation is used to disambiguate keywordlabels across the entire domain. Disambiguation is a well knownrequirement in the art, and there are many approaches to it. It general,disambiguation is used to resolve ambiguities that emerge when entitiesshare the same labels.

In the preferred embodiment, a method of disambiguation is provided byconsolidating keywords into single structural entities that share thesame label. Specifically, if keywords share labels and intersectingdirect concept relationship sets, then there exists a basis forconsolidating the keyword labels, associating them with a single keywordentity.

Alternatively, this method of disambiguation may be relaxed.Specifically, by removing the criterion of intersecting direct conceptrelationship sets, all shared keyword labels in the domain consolidateto the same keyword entities. This is a useful approach when the domainis relatively small or quite focused in its subject matter. Many methodsof disambiguation are known in the art.

The result of this method of keyword extraction is a set of keywords1118, abstracted to the level of “atomic concepts”. The keywords areassociated 1120 with the concepts 306 from which they were derived, asthe preliminary concept definitions 708 a. These preliminary conceptdefinitions 708 a will later be extended to include morpheme entities intheir structure, a deeper and more fundamental level of abstraction.

The entities 708 a derived from this process are passed to subsequentprocesses in the transformation engine. Preliminary concept definitions708 a are the inputs to the morpheme extraction process G, describedbelow and illustrated in FIG. 12 and output data process H, describedbelow and illustrated in FIG. 21.

1.1.2.4 Extract Morphemes

In traditional faceted classification, the attributes for facets aregenerally limited to concepts that can be identified and associated withother concepts using human cognition. As a result, the attributes may bethought of as atomic concepts, in that the attributes constituteconcepts, absent any deeper context.

The methods described herein use statistical tools across large datasets to identify elemental (morphemic), irreducible attributes ofconcepts and their relationships. At this level of abstraction, many ofthe attributes would not be recognizable to human classificationists asconcepts. However, when combined into relational data structures acrossentire domains, they are able to carry the semantic meaning of theconcepts using less information.

FIG. 12 illustrates the method by which morphemes 310 are parsed andassociated with keywords 308 to extend the preliminary conceptdefinitions 708 a. The method of morpheme extraction continues from themethod of generating the preliminary concept definitions, describedabove and illustrated in FIG. 11.

Note that in the preferred embodiment, the methods of morphemeextraction have elements in common with the methods of keywordextraction. Herein, a more cursory treatment is afforded thisdescription of morpheme extraction where these methods overlap.

The pool of keywords 1118 and the sets of direct concept relationships1106 are the inputs to this method.

Patterns are defined to use as criteria for identifying morphemecandidates 1202. These patterns establish the parameters for stemming,and include patterns for whole word as well as partial word matching, asis well known in the art.

As with keyword extraction, the sets of direct concept relationships1106 provide the context for pattern-matching. The patterns are applied1204 against the pool of keywords 1118 within the sets of direct conceptrelationships in which the keywords occur. A set of shared roots basedon stemming patterns are identified 1206. The set of shared rootscomprise the set of candidate morpheme roots 1208 for each keyword.

The candidate morpheme roots for each keyword are compared to ensurethat they are mutually consistent 1210. Roots residing within thecontext of the same keyword and the direct concept relationship sets inwhich the keyword occur are assumed to have overlapping roots. Further,it is assumed that the elemental roots derived from the intersection ofthose overlapping roots will remain within the parameters used toidentify valid morphemes.

This validation check provides a method for correcting errors thatpresent when applying pattern-matching to identify potential morphemes(a common problem with stemming methods). More importantly, thevalidation constrains excessive morpheme splitting and provides acontextually meaningful yet fundamental level of abstraction.

The series of constraints on morpheme and keyword extraction designed inthe preferred embodiment also provide a negative feedback mechanismwithin the context of the complex-adaptive system. Specifically, theseconstraints work to counter-act complexity and manage it within setparameters for classification.

Through this morpheme validation process, any inconsistent candidatemorpheme roots are removed from the keyword sets 1212. The process ofpattern matching to identify morpheme candidates is repeated until allinconsistent candidates are removed.

The set of consistent morpheme candidates is used to derive themorphemes associated with the keywords. As with the keyword extractionmethods, delineators are used to extract morphemes 1214. By examiningthe group of potential roots, one or more morpheme delineators may beidentified for each keyword.

Morphemes are extracted 810 based on the location of the delineatorswithin each keyword label. More significant is the process of derivingone or more morpheme entities to provide a structural definition to thekeywords. The keyword definitions are constructed by relating (ormapping) the morphemes to the keywords from which they were derived1216. These keyword definitions are stored in the domain data store 706.

The extracted morphemes are categorized based on the type of morpheme(as for example, free, bound, inflectional, or derivational) 1218. Inlater stages of the construction process, the rules for buildingconcepts may vary based on the type of morphemes involved and whetherthese morphemes are bound to other morphemes.

Once typed, the extracted morphemes comprise the pool of all morphemesin the domain 1220. These entities are stored in the system's morphemelexicon 206.

A permanent inventory of each morpheme label may be maintained to beused to inform future rounds of morpheme parsing. (For more information,see the overview of the data structure transformations above,illustrated in FIG. 7.)

The morphemes derived from this process are passed to subsequentprocesses in the transformation engine to process morpheme relationshipsI, as described below and illustrated in FIGS. 13-14.

Those of ordinary skill in the art will appreciate that there are manyalgorithms that may be used to discover and extract keyword definitionscomprised of morphemes.

1.1.2.5 Calculate Morpheme Relationships

Morphemes provide one set of elemental constructs that anchor thesystem's multi-tier faceted data structures. The other elementalconstruct are morpheme relationships. As discussed above and illustratedin FIGS. 3-5, morpheme relationships provide a powerful basis forcreating dimensional concept relationships.

However, the challenge is in identifying truly morphemic morphemerelationships in the noise of ambiguity that exists in classificationdata. The multi-tier structure of the present invention provides oneaddress to this challenge. By validating relationships across multiplelevels of abstraction, ambiguity is successively pared away.

The sections that follow provide a second address to the challenge ofdiscovering morpheme relationships. Specifically, methods of patternaugmentation are used to strip away noise to enhance the statisticalidentification of the elemental constructs.

Overview of Potential Morpheme Relationships

FIG. 13 illustrates the method by which potential morpheme relationshipsare inferred from concept relationships in the training set.

Potential morpheme relationships are calculated to examine theprevalence of individual potential morpheme relationships in theaggregate of all concept relationships. Based on this examination,statistical tests may be applied to identify candidate morphemerelationships that have a high likelihood of holding true in the contextof all the concept relationships in which they present.

In the system of the preferred embodiment, potential morphemerelationships are constructed as all permutations of relationships thatmay exist between morphemes in related concepts, wherein theparent-child directionality of the relationships are preserved.

In the example in FIG. 13, a portion of the input concept hierarchy 1008shows a relationship between two concepts. The parent concept and itsrelated child concept contain the morphemes {A, B} and {C, D},respectively.

Again, concepts are defined in terms of one or more morphemes (groupedvia keywords, in the preferred embodiment). As a result, anyrelationship between two concepts will imply at least one (and oftenmore than one) relationship between the morphemes that define theconcepts.

In this example, the process of calculating potential morphemerelationships is illustrated. Four potential morpheme relationships 812a may be inferred from the single concept relationship. Maintaining theparent-child directionality established by the concept relationship, anddisallowing any repetition, there are four potential morphemerelationships that can be derived: A→C, A→D, B→C, B→D.

In general, if the parent concept contains x morphemes and the childconcept contains y morphemes, then there will exist x times y potentialmorpheme relationships: the number of potential morpheme relationshipsis the product of the number of morphemes in the parent and childconcepts.

In the preferred embodiment, this simple illustration of calculatingmorpheme relationships is refined to improve the statistical indicatorsgenerated. These refinements (namely, aligning morphemes) are notedbelow in the description of the method of potential morphemerelationship calculations, illustrated in FIG. 14.

These refinements to the basic method of identifying potential morphemerelationships serve to reduce the number of potential morphemerelationships. This reduction, in turn, reduces the amount of noise,thus augmenting the patterns that identify morpheme relationships, andmakes the statistical identification of morpheme relationships morereliable.

Again, those of ordinary skill in the art will appreciate that there aremany algorithms that may be used to derive potential morphemerelationships from a given set of concept relationships.

Method of Calculating Potential Morpheme Relationships

FIG. 14 presents the preferred embodiment of the process of calculatingpotential morpheme relationships in greater detail.

The intent here is to generate a set of potential morphemerelationships, which will later be analyzed to assess the likelihoodthat they are truly morphemic in nature (that is, they hold in everycontext that they present).

The present method of calculating potential morpheme relationshipscontinues from the method of source structure analytics D, describedabove and illustrated in FIG. 10.

The method also extends from the methods of morpheme extraction I, asdescribed above and illustrated in FIG. 12.

The inputs to this method of determining potential morphemerelationships are the pool of morphemes extracted from the domain 1220and the input concept hierarchy 1008 that contains the validated set ofconcept relationships from the domain.

Morphemes within each concept relationship pair are aligned 1404 toreduce the number of potential morpheme relationships that may beinferred. Specifically, if two data elements are aligned, these elementscannot be combined with any other element in the same conceptrelationship pair. Through alignment, the number of candidate morphemerelationships is reduced.

In the preferred embodiment, axes are aligned based on shared morphemes,and include all morphemes bound to the shared morphemes. For example, ifone concept is “Politics in Canada” and the other is “InternationalPolitics”, the shared morphemes in the keyword “Politics” may be used asa basis for alignment.

Axes are also aligned based on existing morpheme relationships withinthe morpheme lexicon. Specifically, if any given potential morphemerelationship may be represented by morpheme relationships in themorpheme lexicon, either directly or indirectly constructed using setsof morpheme relationships, then the potential morpheme relationship isaligned on this basis.

An external lexicon (not shown in FIG. 14) may also be used to directthe alignment of potential morpheme relationships. WordNet, for example,is a lexicon that may be applied to alignment. A variety of informationcontained within the external lexicon may be used as the basis for thedirection. Under one embodiment, keywords are first grouped by parts ofspeech; potential morpheme relationships are constrained to combine onlywithin these grammatical groupings. In other words, alignment is basedon grammatical parts of speech, as directed by the external lexicon.Direct morpheme relationships that may be inferred from an externallexicon may also be used as a basis for alignment.

The potential morpheme relationships are calculated 812 as allcombinations of morphemes that are not involved in aligned sets. Thiscalculation is described above and illustrated in FIG. 13.

The resultant set of potential morpheme relationships 1406 is held inthe domain data store 910. Here the inventory of potential morphemerelationships is tracked as they present in the training set and arepruned through subsequent stages of analysis.

The potential morpheme relationships derived from this process arepassed to the process for pruning and morpheme relationship assembly J,as described below and illustrated in FIGS. 15-17.

1.1.2.6 Prune Potential Morpheme Relationships

Preferably, the pool of potential morpheme relationships generatedthrough the methods described above and illustrated in FIGS. 13-14 arepruned down to a set of candidate morpheme relationships.

Potential morpheme relationships are pruned based on an assessment oftheir overall prevalence in the training set. Those potential morphemerelationships that are highly prevalent have a greater likelihood ofbeing truly morphemic (that is, of holding the relationship in everycontext).

In addition, morpheme relationships are assumed to be unambiguous intheir relationships with more general (broader) related morphemes. Thestructural marker for this ambiguity is polyhierarchies. Morphemerelationships embody fewer attributes and provide more definite basesfor relating morphemes. As such, potential morpheme relationships mayalso be pruned as they present in polyhierarchies.

To construct a hierarchy of morpheme relationships, it is preferable touse a set of morpheme relationship pairs that are also hierarchical. Assuch, the pool of potential morpheme relationships is analyzed in theaggregate to identify relationships that contradict this assumption ofhierarchy.

The candidate morpheme relationships that survive this pruning processare preferably assembled into morpheme hierarchies. Whereas thecandidate morpheme relationships are parent-child pairings, the morphemehierarchies extend to multiple generations of parent-childrelationships.

FIG. 15A and FIG. 15B illustrate the difference between potentialmorpheme relationships and the pruned set of candidate morphemerelationships.

In FIG. 15A, there are four potential morpheme relationship pairs thatare hierarchical (parent-child). The first three of these relationshipsare relatively prevalent in the domain, but the fourth is relativelyrare. Accordingly, the fourth pair is pruned from the set of potentialmorpheme relationships.

The first three relationship pairs in the set of potential morphemerelationships 1406 are also consistent with the assumption of hierarchy.However, the bi-directional fifth relationships 1502 conflict with thisassumption. The direction of relationship D→C conflicts with therelationship C→D. This morpheme pair is re-typed as related through anassociative relationship and removed from the set of candidate morphemerelationships 1504. FIG. 15B shows the pruned set of candidate morphemerelationships.

1.1.2.7 Assemble Morpheme Relationships

Merging Morpheme Relationships

FIG. 16 illustrates the consolidation of candidate morphemerelationships into an overall morpheme polyhierarchy. All candidatemorpheme relationship pairs are incorporated into one aggregate set,connecting logically consistent generational trees (as described in moredetail below).

This data structure is described as a “polyhierarchy” since it mayresult in singular morphemes involved in more than one directrelationship with more general morphemes (multiple parents). Thispolyhierarchy will be transformed into a strict hierarchy (singleparents only) in later stages of the process.

The potential morpheme relationships that survive the conflict pruningprocess (described above and illustrated in FIG. 15B) are collected intoa set of candidate morpheme relationships 1504. Preferably, the set ofcandidate morpheme relationships should be merged into an overallmorpheme polyhierarchy 1602.

In the preferred embodiment, the constraints on the process ofconstructing the overall polyhierarchy are: 1) that the set of candidatemorpheme relationships in the polyhierarchy is logically consistent inthe aggregate; 2) that the polyhierarchy uses the least number ofpolyhierarchical relationships necessary to create a logicallyconsistent structure.

A recursive ordering algorithm may be used to assemble the trees andhighlight conflicts and proposed resolutions. The reasoning applied tothe following example illustrates the logic of this algorithm.

Based on relationship hierarchy #1, A is superior (that is, moregeneral) than C. Based on hierarchy #2, B is superior to C. Based onhierarchy #3, A is superior to D. The four morphemes can be logicallycombined with A and B superior to C, and A superior to D.

Where more than one logical ordering is possible, the concept generalityindex 1012 is used to resolve the ambiguity. (The concept generalityindex is created through a method of source structure analytics,described above and illustrated in FIG. 10.) This index is used tocompare morphemes to assess whether morphemes are relatively moregeneral or more specific than other morphemes (with the generalitymeasured in terms of the degrees of separation from the root nodes).

In the example, both A and B are logically consistent topmost nodesbased on the set of candidate morpheme relationships. A and B are alsoboth parent to C. Thus, a polyhierarchical set of relationships isgenerated at C. Since there is no information in the sample set toconflict with the polyhierarchical set of relationships, therelationships are assumed valid. Processing would continue to resolvethe polyhierarchies in later stages.

If new data presented that indicated that A and B were instead relatednodes through indirect relationships, then the system would resolve thepolyhierarchy immediately and order A and B in the same tree. Thepriority of A and B would be determined through the generality index.Here, A has a lower generality ranking than B. It is thus accorded ahigher (more general) position in the resultant polyhierarchy 1602.

Morpheme Polyhierarchy Assembly

FIG. 17 illustrates a method by which the morpheme polyhierarchy may beassembled from the candidate morpheme relationships.

The morpheme hierarchy is assembled by analyzing the candidate morphemerelationship pairs in the aggregate. As in input concept hierarchyassembly, the objective is to consolidate the individual pairs ofrelationships into a unified whole.

The method of morpheme relationship assembly continues from the methodof calculating the potential morpheme relationships J, described aboveand illustrated in FIG. 13-14.

The set of potential morpheme relationships 1406 is the input to thismethod. The candidate morpheme relationships are sorted 1702 based on ananalysis of the concept relationships that contain the morphemes. Theconcept relationships are sorted based on the aggregate count ofmorphemes in each concept relationship pair (lowest to highest).

Morpheme relationships increase in likelihood as the number of morphemesinvolved in the concept relationship pair decreases (since theprobability for any given morpheme relationship candidate is factored bythe number of potential candidates in the pair). Therefore, in thepreferred embodiment, the operations prioritize the analysis of conceptrelationships with lower morpheme counts. Lower the number of morphemesin the pair and you increase the chances of finding a truly morphemicmorpheme relationship.

Parameters to define the statistically relevant boundaries of morphemerelationships are set 1704. These parameters are based on the prevalenceof the morpheme relationships in the aggregate. The object is toidentify those that are highly prevalent in the domain. Theseconstraints on the morpheme relationships also contribute to thenegative feedback mechanism of the complex-adaptive system. An analysisof the relationship set 1706 in the aggregate is conducted to determinethe overall prevalence of each relationship. This analysis maypreferably combine statistical tools conducted within sensitivityparameters controlled by system administrators. The exact parameters aretailored to each domain and may be changed by domain owners and systemadministrators.

As with the concept relationship analysis, circular relationships 1708are used as a structural marker to negate the assumption of hierarchicalrelationships. Potential morpheme relationships are pruned if they donot pass the filters of prevalence and hierarchy 1710.

The pruned set of potential morpheme relationships comprises the set ofcandidate morpheme relationships 1504. The generality of the morphemes1010 a is inferred from the generality of the source structure concepts,as embodied in the concept generality index 1012.

Concepts embodying the lowest numbers of morphemes are used assurrogates for the generality of each morpheme. To illustrate the basisof this assumption, assume that a concept is comprised of only onemorpheme. Given the high degree of relatedness between the concept andthe single morpheme that comprises it, it is likely that the generalityof the morpheme would closely correlate to the generality of theconcept.

This reasoning directs the calculation of morpheme generality in thepreferred embodiment. Specifically, the system gathers the set ofconcepts that embody the lowest number of morphemes in the aggregate.That is, the system selects a set of concepts that represents allmorphemes in the set.

The concept generality index 1012 is to be used to prioritizedimensional concept relationships and is preferably stored (not shown)in the domain data store 706.

Morpheme hierarchies are assembled into an overall polyhierarchystructure 1712, using a method as described above and illustrated inFIG. 16. This involves ordering the nodes in the aggregate and removingany redundant relationships that may be inferred from other sets ofindirect relationships. The concept generality index created is used toorder the morphemes from most general to most specific.

Those of ordinary skill in the art will appreciate that there are manyalgorithms that may be used to merge a collection of hierarchicalmorpheme relationships into a polyhierarchy, as is known in the art.

1.1.2.8 Assemble Morpheme Hierarchy

FIGS. 18A-20 illustrate the transformation of the morpheme polyhierarchyinto a morpheme hierarchy.

Morpheme Polyhierarchy Attribution

FIGS. 18A-18B illustrate a process of morpheme attribution and exampleresults. Attribution in this context refers to the manner in which facetattributes are ordered and assigned to data elements. Just as theoperations place constraints on entity extraction (such as keyword andmorpheme extraction), the morpheme hierarchy is built using explicitconstraints on morpheme relationships.

The morpheme relationships that link morphemes into hierarchies are, bydefinition, morphemic. Morphemic entities are fundamental andunambiguous. Morphemes must thus relate to only one parent. In a set ofmorpheme relationships (the morpheme hierarchy), morphemes can exist inonly one location.

Based on these definitions in the preferred knowledge representationmodel, morphemes can be presented as attributes within facet hierarchiesof morphemic data. The knowledge representation model thus provides forthe faceted data and multi-tier enhanced method of facetedclassification.

In the preceding methods, the aggregation of candidate morphemerelationships may present sets of morpheme polyhierarchies 1802. Thus,attribution is used to weigh these conflicts in the knowledgerepresentation model and resolve solutions 1804.

The method of attribution in the preferred embodiment involves finding aplace for each morpheme in the hierarchy that does not conflict with themorphemic requirements of hierarchy.

Morphemes in polyhierarchies may ascend to new positions within theiroriginal trees or moved to entirely new trees. This process ofattribution ultimately defines the topmost root morpheme nodes in thefacet hierarchy. Thus, the root morpheme nodes in the morpheme hierarchyare defined as the morpheme facets, with each morpheme contained withinthe morpheme facet attribute trees.

The following discussion illustrates the method for removing multipleparents using the concept of attributes.

Again, the structural marker for the conflict is the presence ofmultiple parents presenting in the morpheme polyhierarchy 1802. Toremove the conflicts, morphemes with multiple parents are reconsideredas attributes of the ancestors of the shared parents.

Preferably, attribute classes are created to maintain the grouping ofthe parents originally shared by the reorganized morpheme and to keepthe morpheme in a separate attribute class from those parents. (In caseswhere there is no unique ancestor, the method promotes the morphemes tothe root level of the hierarchy, as a new morpheme facet.)

Preferably, relationships are reorganized into attribute classes fromthe root nodes to the leaf nodes. Multiple parents are first reorganizedinto attributes so that a singular parent can be identified. That is,top-down traversal of the morpheme relationships provides forattribution that resolves to a solution set 1804.

Generally, if two morphemes share at least one parent, they are siblingsin the context of that shared parent. Sibling child nodes may be groupedunder a single attribute class. (Note that the child nodes need onlyshare one parent; they need not share all parents.) If morphemes do notshare at least one parent, they are grouped as separate attributes ofthe shared ancestor.

To choose between alternatives, we weigh the relevance of the sourcerelationships. Measures of relationship relevance were introduced abovein the discussion of source structure analytics, illustrated in FIG. 10.

Starting from the top-down, the transforming steps breakdown as follows:

-   1. The sibling group {B, C, D, F, H} share a single parent, A. Each    individual node would be checked to see if there are multiple    parents. In this case, none of these nodes have multiple parents, so    there is no need to reorganize these relationships.-   2. The morpheme E has multiple parents. The closest single-parent    ancestor of E is A. E needs to be reorganized as an attribute of A.-   3. The parents of E, {B, C, D, F, H} are grouped under the attribute    class, A1. E then becomes a sibling of A1, as an attribute of A.-   4. The morpheme G also has multiple parents. As in steps (2-3), it    needs to be reorganized as an attribute of A. In addition, since E    and G share at least one parent, they can be grouped under a single    attribute class, A2.-   5. The morpheme, J, has a unique parent, H. This parent-child    relationship does not need to be reorganized.-   6. The morpheme, K, has multiple parents, E and G. The unique    ancestor of E and G is now, A2. K needs to be reorganized as an    attribute of A2.-   7. The parents of K, {E, G} are grouped under the attribute class,    A2-1. K then becomes a sibling of A2-1, as an attribute of A2.

The end result is the morpheme hierarchy, conforming to the assumptionsof truly morphemic attributes and morpheme relationships defined by theknowledge representation model of the invention.

Morpheme Hierarchy Reorganization

FIG. 19 presents the recursive algorithm that provides for the method ofattribution in the preferred embodiment. The core logic of this morphemehierarchy reorganization is the method of attribution described aboveand illustrated in FIG. 18.

The inputs for this method are the morpheme polyhierarchy K, asdescribed above and illustrated in FIGS. 15-17. The input to the presentmethod is the morpheme polyhierarchy 1602. Relationships are sorted fromroot nodes to leaf nodes 1902. Each morpheme in the morphemepolyhierarchy is checked for multiple parents. Herein, the morpheme thatis the focus of the analysis is known as the active morpheme.

If any multiple parents exist, the set of multiple parents for theactive morpheme are grouped into sets, hereafter the morpheme attributeclasses 1906. The morpheme attribute classes are used to direct how themorphemes in the reorganized tree should be ordered.

For each morpheme attribute class, a unique ancestor is located 1908that does not have a multiple parent. Preferably, the ancestor isuniquely associated with only the attribute class (group of parentsshared by the morpheme).

If the ancestor exists, the system creates one or more virtualattributes 1910 to contain all the morphemes in the morpheme attributeclass. This node in the tree is called a “virtual attribute” because itis not associated with any morpheme directly and will thus not beinvolved in any concept definitions. It is a virtual attribute, not areal attribute.

If the ancestor exists and one or more attributes are created, theactive morpheme is reorganized as an attribute of the ancestor 1912,either directly related to the ancestor or grouped with other morphemesin a morpheme attribute class.

If the unique ancestor does not exist, the morpheme is repositioned as aroot node (facet) in the tree 1914.

The system also allows administrators to manually alter 1916 the pool ofmorpheme relationships and the resultant morpheme hierarchy to refine ordisplace the results generated automatically.

The end result of this process is the morpheme hierarchy 402, whichcomprises a hierarchical arrangement of elemental morphemes. One of theelemental constructs of the system's data structure, the morphemehierarchy is used to categorize and arrange the entities into increasingcomplex levels of abstraction.

The morpheme relationships in the morpheme hierarchy are entered in themorpheme lexicon 206. Morpheme labels are assigned to the morphemesbased on the prevalence of labels stored in the system. The morphemelabel that is most prevalent in the system is used as the singlesignature label for that morpheme.

The outputs of this method are processed as system output data L, asdescribed below and illustrated in FIG. 21.

Alternative manners to transform a polyhierarchy to a strict hierarchymay be used. A single parent may be chosen based on any of a number ofweighting factors to remove a multi-parent situation. In a simplesolution, multi-parent relationships may be deleted.

FIG. 20A illustrates a sample tree fragment from the assembled morphemehierarchy. Each node in the tree (e.g. 2002 a) represents a morpheme inthe morpheme hierarchy. The folder icons are used to indicate morphemesthat are parents to related morphemes nested underneath (morphemerelationships). The texts next to each node (e.g. 2002 b) are theassociated morpheme labels (in many cases, partial words).

1.1.3 Build Dimensional Structure

Here begins the process of building (or synthesizing) the dimensionalconcept taxonomy 210 based on the enhanced method of facetedclassification. This classification generates dimensional conceptrelationships through the union of the morpheme hierarchy with the setof concept definitions (more specifically defined in terms of themorphemes, with zero or more morphemes as morpheme attributes within themorpheme hierarchy).

The enhanced method of faceted classification is applied at multipletiers of data abstraction. In this way, multiple domains may share thesame elemental constructs for classification, while maintainingdomain-specific boundaries.

1.1.3.1 Process Output Data

The following points summarize the steps involved in synthesizing thefaceted classification data structure (as further described below):

Preferably, for each domain to be classified, output the data structuresas the domain-specific keyword hierarchy and the set of domain-specificconcept definitions (more specifically defined in terms ofdomain-specific keywords, with zero or more domain-specific keywords askeyword attributes within the domain-specific keyword hierarchy).

The domain-specific faceted data described above may be derived fromelemental constructs shared across domains. The preliminary conceptdefinitions are revised and significantly extended with new information.This is accomplished by comparing the information in the morphemehierarchy with the original concept relationships in the training set.

Specifically, the synthesizing operations assign concept definitions tocontent nodes based on an analysis of not only the explicit definitionsprovided by domain owners, but also through an analysis of allintersecting concepts and concept relationships in the aggregate. Apreliminary definition of “explicit” attributes is assigned, which islater supplemented with a far richer set of attributes “implied” by theconcept relationships that intersect with the content nodes.

The candidate morpheme relationships are assembled into an overallmorpheme hierarchy, to be used as the data kernel for the facetedclassifications. A separate facet hierarchy for each domain is createdfrom the unique intersections of keywords in each domain and theirmorphemes. This data structure is the expression of the morphemehierarchy limited to the boundaries of the domain.

The facet hierarchy is expressed in the vocabulary of the domain (itsunique set of keywords) and includes only those morpheme relationshipsthat factor into the domain. The faceted classification for each domainis outputted as the set of concept definitions for that domain and thefacet hierarchy.

Thus, in the preferred embodiment, the domain-specific facet hierarchiesare inferred from the centralized morpheme hierarchy. It provides for aricher set of facets for smaller domains. It builds on the sharedexperiences of multiple domains (which may correct for errors thatpresent in smaller domains). And it facilitates faster processing ofdomains.

In another embodiment, the system could create a unique facet hierarchyfor the domain based directly on the methods described above,illustrated in FIGS. 18-19.

FIGS. 20A and 20B illustrate tree fragments from the assembled morphemehierarchy 2002 (as described above) and tree fragments from thedomain-specific keyword hierarchy 2004 as derived in the preferredembodiment. Note that in the tree fragment for the keyword hierarchy2004, texts next to each node (e.g. 2004 b) representing the associatedkeyword labels are full words as they would present in the domain.Further, the tree fragment for the keyword hierarchy 2004 is a subset ofthe tree fragment for the morpheme hierarchy 2002, contracted to includeonly those nodes relevant to the domain for which the keyword hierarchyis derived.

FIG. 21 illustrates the operations of preparing the output data for theenhanced method of faceted classification.

The output data is comprised of the revised concept definitions and akeyword hierarchy for the domain. The keyword hierarchy is based on themorpheme hierarchy.

Inputs to this process are the set of content nodes 302 to beclassified, the input concept hierarchy 1008, the morpheme hierarchy402, and the preliminary concept definitions 708 a. Respectiveoperations C, E, L and H to generate or otherwise obtain these inputsare described above.

The intersection of morpheme attributes within the first conceptdefinition 708 a and input concept relationships are used 2102 to revisethe first concept definition 708 a to a second concept definition 708 b.Specifically, if concept relationships in the source data cannot beinferred from the morpheme hierarchy, then the concept definitions areextended to provide for attributes “implied” by the conceptrelationships. The result is the set of revised concept definitions 708b.

Identify the set of relevant morpheme relationships 2106 in the morphemehierarchy from the set of all morphemes participating in the domain.

The morphemes in the reduced and domain-specific version of the morphemehierarchy are labeled using keywords from the domain 2108. For eachmorpheme, select a signature keyword that uses that morpheme thegreatest number of times. Assign the most prevalent keyword label foreach keyword. Individual keywords are limited to one occurrence in thefacet hierarchy. Once a keyword is used as a signature keyword, it isunavailable as a surrogate for other morphemes.

The morpheme hierarchy is consolidated into a set of morphemerelationships that includes only the morphemes participating in thedomain and the keyword hierarchy 2112 is inferred 2110 from theconsolidated morpheme hierarchy.

The output data 210 a representing the faceted classification iscomprised of the revised concept definitions 708 b, the keywordhierarchy 2112, and the content nodes 302. The output data istransferred to the domain data store 706.

The concept relationships in the input concept hierarchy also directlyaffect the output data in the domain data store 706. Specifically, theinput concept hierarchy may be used to prioritize the relationshipsinferred from the synthesis portion of the operations. The pool ofconcept relationships drawn directly from the source data represents“explicit” data, as opposed to the dimensional concept relationshipsthat are inferred. Relationships inferred that are explicit in the inputconcept hierarchy (directly or indirectly) are prioritized overrelationships that did not present in the source data. That is, explicitrelationships may be deemed more significant than the additionalrelationships inferred from the process.

The output data is now available as a complex dimensional data structureto render the dimensional concept taxonomy M.

1.1.3.2 Construct Concept Relationships

The organizing principles of the enhanced method of facetedclassification are illustrated in FIGS. 3-5, first introduced above, anddescribed in more detail below, illustrated in and FIGS. 22-24. In thepreferred embodiment, both explicit and implicit morpheme relationshipscan be combined with contextual investigations of the domain to infercomplex dimensional relationships in the dimensional concept taxonomy.

In the preferred embodiment of the invention, the interplay of thestructural entities of the knowledge representation model (describedabove) establish logical links between morphemes, morphemerelationships, concept definitions, content nodes, and conceptrelationships, as follows:

Dimensional concept relationships that are inferred directly from thefacet hierarchy are known herein as explicit relationships. Dimensionalconcept relationships that are inferred from intersecting sets of facetattributes within concept definitions assigned to the content nodes tobe classified are known as implicit relationships.

Preferably, concept definitions are described using morphemes as facetattributes. As described above, it does not matter whether the facetattributes (morphemes) are explicit (“registered” or “known”) in thelexicon or implicit (“not registered” or “unknown”). There should simplybe a valid description associated with the concept definition to carryits meaning in the dimensional concept taxonomy. Valid conceptdefinitions provide raw materials to describe the meaning of the contentnodes in the dimensional concept taxonomy. In this way, objects in thedomain may be classified in the dimensional concept taxonomy whether ornot they were previously analyzed as part of the training set. As iswell known in the art, there are many methods and technologies availableto assign concept definitions to objects to be classified.

Explicit relationships between concepts are calculated by examining therelationships between the attributes in their concept definitions. Ifconcept definitions contain attributes that are related either directlyor indirectly in the facet hierarchy (hereafter, of the same “lineage”)to those in the content node being classified (hereinafter, the “activenode”), then explicit relationships exist between the concepts along thedimensional axis represented by the attributes involved.

Subject to limiting constraints (described below), implicitrelationships are inferred between any concepts that share a subset ofattributes in their concept definitions. The intersecting set ofattributes establishes a parent-child relationship. Directionality(priority) within the implicit hierarchy is determined by examining thegenerality of any attributes in the facet hierarchy.

Axes are defined in terms of facet attribute sets. In the preferredembodiment, axes are defined by the set of facets (root nodes) in thefacet hierarchy. These attribute sets can then be used to filterconcepts into consolidated hierarchies of dimensional conceptrelationships. Alternatively, any set of attributes may be used as basesof dimensional axes, for dynamically constructed (custom) hierarchiesderived from the complex dimensional structure.

Preferably, a dimensional concept relationship exists if and only ifexplicit and/or implicit relationships may be drawn for all axes in theparent concept definition. Thus dimensional concept relationships arestructurally intact across all dimensions defined by the attributes.

1.1.3.3 Implicit Relationships

If concepts within the active content node contain facet attributes(preferably and hereafter, as morphemes) of the same lineage as those inother content nodes (hereinafter “related nodes”), then relationshipsexist between the concepts of the active and related nodes. In otherwords, each concept inherits all the relationships inferred by therelationships between their morphemes, as existing in the content nodes.

The process of calculating implicit relationships assumes that anycontent nodes that share all or a subset of morphemes from their conceptdefinitions are related. The intersecting set of morphemes establishes aparent-child relationship.

Priorities within implicit relationships are determined first byexamining the overall priorities of any registered morphemes within thesets in question. The topmost registered morpheme establishes thepriority for the set.

For example, if the first set includes three registered morphemes withpriority numbers {3, 37, 303}, the second set includes two registeredmorphemes with priorities {5, 490}, and the third set includes threeregistered morphemes with priorities {5, 296, 1002}, then the sets wouldbe ordered: {3, 37, 303}, {5, 296, 1002}, {5, 490}. The first orderedset is prioritized based on the top overall ranking of the morpheme withpriority 3 contained in its set. The latter two sets both have a topmostmorpheme priority of {5}. Therefore, the next highest morphemepriorities in each set are examined to reveal that the set containingthe morpheme with priority {296} should be the higher prioritized set.

Where the content nodes in the implicit relationships are notdifferentiated by the registered morphemes, the system uses the numberof implicit morphemes as the basis for prioritization. The set with thefewest number of morphemes is assumed to be of a higher priority in thehierarchy. Where content nodes contain the same explicit morphemes andthe same number of unregistered implicit morphemes, the content nodesare considered at parity with each other. When content nodes are atparity, priority is established by the order in which each of thesecontent nodes is discovered by the system.

FIG. 22 provides a simple illustration of the preferred embodimentconstruction of the implicit relationships

In this example, the morpheme “business” 2201 is registered in themorpheme lexicon. Assume that through user interactions, a content nodeis constructed with a concept definition that contains this morpheme,plus a new morpheme, “models” 2202, that is not recognized in themorpheme lexicon.

Continuing the example above, the morpheme “business” has the highestpriority 2203. The set “business, models” is an implied child of“business” 2204. Any additional morphemes that are added to this set,such as “advertising” 2205, would create additional layers in thehierarchy 2206.

Any single morpheme, whether explicit in the system or implied, can beused as a basis for a classification hierarchy (or axis). Continuing theexample above, the implicit morpheme “advertising” 2207 is the parent2208 of a hierarchy based on this morpheme. The set “business, models,advertising” 2205 is a child 2209 in this hierarchy. Any additional setthat includes “advertising” would also be a member of this hierarchy. Inthe example, the set “advertising, methods” 2210 is also a child toadvertising 2211. Since the morpheme “business” is registered, the set“business, models, advertising” is given a higher priority in theadvertising hierarchy over the set “advertising, methods”, whichcontains only implicit morphemes.

1.1.3.4 Axial Definitions and Structural Integrity

Another rule for building the dimensional concept taxonomy in thepreferred embodiment of the system concerns the structural integrity ofthe dimensional axes. Each morpheme (attribute) in a concept definitionmay establish a dimensional axis. Dimensional concept relationshipsinferred from these morphemes must be structurally intact across alldimensions as determined by the parent node. In other words, alldimensions that intersect with the parent concepts must also intersectall the child concepts of the node. The following example willillustrate:

Consider the active content node with the concept definition {A, B, C},

-   -   Where A, B, C are three morphemes in a concept definition, and        the morphemes E, F, G are children of A, B, C, respectively, in        the morpheme hierarchy;    -   {A, B, C} refers to a concept definition described with        morphemes A and B and C    -   {A, *} refers to a combination of explicit morpheme A and        implicit morpheme(s) {*} to establish a node that is an implicit        child of A    -   {A|B} refers to either the morpheme {A} or {B}.

The three morphemes A, B, C in the active node establish threedimensions (or intersecting axes) in the dimensional concept hierarchy.For any other content nodes to be a child of this node, candidates mustbe children relative to all three axes. The notation that follows is thesolution set of explicit and implicit relationships as defined by thepreferred embodiment of the invention:

-   -   {(A|E|A,*|E,*), (B|F|B,*|F,*), (C|G|C,*|G,*)},    -   Where the morpheme of the first dimension is A or E or an        implicit morpheme of A or an implicit morpheme of E;    -   where the morpheme of the second dimension is B or F or an        implicit morpheme of B or an implicit morpheme of F;    -   where the morpheme of the third dimension is C or G or an        implicit morpheme of C or an implicit morpheme of G.

The combination of explicit and implicit relationships in the morphemesthus establishes the rules for building hierarchical relationshipsbetween concepts.

As is known in the art, there are many ways to optimize these types offiltering and ordering functions. They include data management toolssuch as indices and caches. These refinements are well known in the artand will not be discussed further herein.

The facet hierarchy (as expressed by the morpheme hierarchy) is used toprioritize the content nodes. Specifically, each content node embodiesattributes that present in at most one location in the facet hierarchy.The priority of the attributes in the hierarchy determines the priorityof the nodes.

An alternate embodiment of node prioritization concerns “signature”nodes. These are defined as the content nodes that best describe (orgive meaning) to their associated concepts. For example, a domain ownermay associate a photograph with a specific concept as the signatureidentifier for that concept. Signature nodes may thus be prioritized.

There are many ways to implement signature nodes. For example, labels,as a special class of content nodes, are one way. A special attributemay be assigned to signature nodes and that attribute may be given thehighest priority in the facet hierarchy. Or a field may be used in thetable of content nodes to stipulate this attribute.

The prioritization based on the facet hierarchy may be supplemented byautomatic bases such as alphabetization, numerical, and chronologicalsorting. In traditional faceted classification, prioritization andsorting are issues of notation and citation order. Systems typicallyprovide for a dynamic reordering of the attributes for prioritizationand sorting. Therefore, no further discussion of these operations ismade here.

1.1.3.5 Method of Building Concept Taxonomy

As described above, a single content container or content node (such asa Web page) may be assigned more than one concept. Each concept will bea member of one hierarchy for each morpheme it contains. Consequently, asingle content container or content node may reside on many discretehierarchies in the dimensional concept.

FIG. 23 illustrates the process in the preferred embodiment by which theoutput data for the faceted classification produces the dimensionalconcept taxonomy 210 to reorganize the domain. The output data isgenerated M (as described above and illustrated in FIG. 21). The inputsfor this method are the revised concept definitions 2104, the keywordhierarchy 2112, and the content nodes 302 from the domain.

Each concept definition 708 b is mapped to keywords 2302 in the keywordhierarchy 2112. New dimensional concept relationships for the conceptsare generated 820 by the rules of explicit and implicit relationshipconstruction, as described above and illustrated in FIGS. 3-5, and 22.

Preferably, the scope of processing is limited to the relationshipsproximate to the area of the dimensional structure in focus by theend-user or end-process (discussed below).

Administrators of the information structure may prefer to manuallyadjust 2304 the results of the automatically generated dimensionalconcept taxonomy construction. Preferably, the operations support thesetypes of manual interventions but do not require user interactions forthe fully automated operation.

Preferably, an analysis 2306 is used to assess the parameters of theresultant dimensional concept taxonomy. Again, statistical parameterspreferably are set 2308 by the administrators as scaling factors for thedimensional concept taxonomy. They may also limit the complexity asnegative feedback in the complex-adaptive system by reducing the scopeof processing, and thus scale back the number of hierarchies that areincorporated.

The dimensional concept taxonomy 210 is available for user interactionsN, as described below and illustrated in FIG. 29.

Note that the data structure that derives the dimensional concepttaxonomy 210 may be represented in many ways, for many purposes. In thedescription that follows, there is illustrated the purpose of end-userinteractions. However, these structures may also be used in the serviceof other data manipulation technologies, for example as an input toanother information retrieval or data mining tool (not shown).

1.1.3.6 Scope of Domain Processing

As the size of the domain and facet hierarchy increase, the number ofdimensional concept relationships that may be inferred grows rapidly.Limits may be placed on the number of relationships generated.

In one embodiment, all content nodes in the domain are examined andcompared before a complete view of the dimensional concept taxonomy isgenerated. In other words, the system discovers all the content nodes inthe domain that may be related before any inferences are made about thedirect hierarchical relationships between these related nodes.

In another embodiment, instead of analyzing the entire domain, alocalized region of the domain is analyzed based on the users' activefocus. This localized analysis may be applied to materials whether ornot they were analyzed previously as part of the training set.Parameters are set by administrators to balance the depth of analysiswith the processing time (latency).

FIG. 24 illustrates the selection of candidate content nodes from thedomain and the ordering of those content nodes into dimensional concepthierarchies. A constrained view of the domain relative to active node2402 is preferably taken. Rather than processing the entire domain,operations may do a directed investigation of all content nodes (e.g.2406) in the immediate proximity 2404 of the active node 2402. Proximitymay be determined using morpheme lineages (extended relationshipsbetween morphemes) as stored in the morpheme lexicon. In this way,meaningful and comprehensive information may be provided in a specificcontext of the domain, without expending processing costs on the entiredomain.

Recursive algorithms are useful to sub-divide this undifferentiatedgroup of related content nodes into specific structural groups. Thegroups are described relative to the active container, as parents,children, and siblings. The structural relationships described by thesegroups are well known in the art. These proximate nodes are then orderedinto hierarchical relationships relative to the active node, based onthe underlying morpheme relationships and morphemes involved.

For materials that were not analyzed as part of the training set, thesystem would use the operations of the localized analysis to classifymaterials under the enhanced faceted classification scheme derived fromthe training set materials.

FIG. 25 illustrates the operations of classifying a local subset ofmaterials from the domain that were not part of the training set used todevelop the faceted classification scheme.

From the domain 200 a local subset of the domain materials 2404 a isselected for processing. The materials are selected based on selectioncriteria 2502 established by the domain owners. The selection is maderelative to the active node 2504 that is the basis for the localizedregion. The selection process generates the parameters of the localsubset 2506, such as a list of search terms that describe the boundariesof the local subset.

There are many possible selection criteria for the local set. In oneembodiment, the materials are selected by passing the concept definitionassociated with the active node to a full-text information retrieval(search) component to return a set of related materials. Such full-textinformation retrieval tools are well known in the art. In an alternateembodiment, an extended search query may be derived from the conceptdefinition in the active node by examining the keyword hierarchy toderive sets of related keywords. These related keywords may in turn beused to extend the search query to include terms related to the conceptdefinition of the active node.

The local subset of the domain 2404 a derived from the selection processcomprises the candidate content nodes to be classified. For eachcandidate content node in the local subset, a concept signature isextracted 2508. The concept signatures are identified by the domainowners and are used to map keywords 2302 in the domain-specific keywordhierarchy 2112 to provide concept definitions for each candidate contentnode. Again, the build component does not require that all keywordsderived from the concept signatures are known to the system (asregistered in the keyword hierarchy).

Concept hierarchies are calculated 820 for the candidate content nodesusing the build rules of implicit and explicit relationships describedabove. The end result is a local concept taxonomy 210 c, wherein thecontent nodes from the local subset of the domain are organized underthe constructive scheme derived for that domain from the training set.The local concept taxonomy is then available as an environment for userinteractions to further refine the classification.

Note that the operations of classifying a local subset of materials fromthe domain, as described above, may also be used to classify newdomains. In other words, the training set from one domain may be used asthe basis for a constructive scheme to classify materials from a newdomain, thus supporting a multi-domain classification environment.

1.1.4 User Interactions

The dimensional concept taxonomy provides an environment for userinteractions. In a preferable embodiment, there is provided two mainuser interfaces. A navigation “viewer” interface provides for browsingthe faceted classification. This interface is of a class known as“faceted navigation”. The other interface is known as an “outliner”,which allows end users to change the relationship structure, conceptdefinitions, and content node assignments.

The general features of faceted navigation and outliner interfaces arewell known in the art. Novel aspects described herein below,particularly as they related to the complex-adaptive system 212, will beapparent to those of skill in the art

1.1.4.1 Viewing the Concept Taxonomy

The dimensional concept taxonomy is expressed through the presentationlayer. In the preferred embodiment, the presentation layer is a website. The web site is comprised of web pages that render a set of viewsof the dimensional concept taxonomy. The views are portions (e.g. asubset of the polyhierarchy filtered by one or more axis) of thedimensional context taxonomy within the scope of an active node. Theactive node in this context is a node within the dimensional concepttaxonomy that is presently in focus by the end-user or domain owner. Inthe preferred embodiment, a “tree fragment” is used to represent theserelationships.

Users may provide text queries to the system to move directly to thegeneral area of their search and information retrieval. Views may befiltered and sorted by the facets and attributes that intersect witheach concept, as is well known in the art.

Content nodes are categorized by each concept. That is, for any givenactive concept, all content nodes that match the attributes of thatconcept as filtered by the user are presented. The “resolution” of eachview may be varied around each node. This refers to the breadth ofrelationships displayed and the exhaustiveness of the survey. The issueof the resolution of the view may also be considered in the context ofthe size and selection of the domain portion that is analyzed. Again,there is a trade-off between the depth of the analysis and the amount oftime it takes to process. The presentation layer operates to select aportion of the domain to be analyzed based on the location of the activenode, the resolution of the view, and parameters configured byadministrators.

FIG. 26 provides an illustrative screen capture of the main componentsof the dimensional concept taxonomy presentation UI for end-user viewingand browsing.

The content container 2600 holds the various types of content in thedomain, along with the structural links and concept definitions thatform the presentation layer for a dimensional concept taxonomy. One ormore concept definitions are associated with the content nodes in thecontainer. The system is able to manage any type of informationalelement, registered in the system along with a URI and the conceptdefinitions used to calculate dimensional concept relationships, asdescribed herein.

In the preferred embodiment, user interface devices that are usuallyassociated with traditional linear (or flat) information structures arecompounded or stacked to represent dimensionality in the complexdimensional structures.

Compounding traditional Web UI devices such as navigation bars,directory trees 2604, and breadcrumb paths 2602 are used to show thedimensional intersections at various nodes in the informationarchitecture. Each dimensional axis (or hierarchy) that intersects withthe active content node 2606 may be represented as a separate hierarchy,one for each intersecting axis.

Structural relationships are defined by pointers (or links) from theactive content container to related content containers in the domain.This provides for multiple structural links between the active containerand the related containers, as dictated by the dimensional concepttaxonomy. The structural links may be presented in a variety of ways,including a full context presentation of the concepts, a filteredpresentation of the concepts that displays only the keywords on theactive axis, a presentation of content node labels, etc.

Structural links provide the context for the content nodes 2608 withinthe dimensional concept taxonomy, organized in prioritized groupings ofcontent nodes within one or more relationship types (for example,parent, child, or sibling).

XSLT is used to present structural information as a navigation path onthe Web site, allowing a user to navigate the structural hierarchy tocontainers related to the active container. This type of presentation ofstructural information as navigation devices on a web site would beamong the most basic applications of the system.

These and other navigational conventions are well known in the art andwill not be discussed further herein.

There are many methods and technologies that may be used to presentmulti-dimensional information structures and provide interactivity toend-users. For example, multivariate forms may be used to allow users toquery the information architecture along many different dimensionssimultaneously. Technologies such as “pivot tables” may be used to holdone dimension (or variable) constant in the information structure whileother variables are changed. Software components such as ActiveX may beembedded in the Web pages to provide interactivity with the underlyingstructure. Visualization technologies may provide three-dimensionalviews of the data. These and other variations will be apparent to thoseskilled in the art and do not limit the scope of the present invention.

1.1.4.2 Editing the Concept Taxonomy

The presentation layer distils the dimensional structure down tosimplified views (such as web pages that include links to related pagesin the dimensional concept taxonomy) that are necessary for humaninteraction. As such, the presentation layer may also double as theediting environment for the informational structures from which it isderived. In the preferred embodiment, the user is able to switch toediting mode from within the presentation layer to immediately edit thestructures.

An outliner provides the means for users to manipulate hierarchicaldata. The outliner also allows users to manipulate the content nodesthat are associated with each concept in the structure.

Preferably, user interactions alter the context and/or the conceptsassigned to the nodes in the dimensional concept taxonomy. Contextrefers to the position of a node relative to the other nodes in thestructure (that is, the dimensional concept relationships that establishstructure). Concept definitions describe the content or subject matterof the node, expressed as collections of morphemes.

The user is presented with a review process in the preferred embodiment,to enable the user to confirm the parameters of such user's edits. Thefollowing dimensional concept taxonomy information is preferably exposedto the user for this review: 1) the content of the node; 2) the morphemegroups (expressed as keywords) associated with the content; and 3) theposition of the node in the taxonomic structure. The user is able toalter the parameters of the latter two (morphemes and relativepositioning) to make the information consistent with the first (thecontent at that node).

Thus, interactions in the preferred embodiment of the invention may besummarized as some combination of two broad types: a) container edits;and b) taxonomy edits.

Container edits are changes to the assignment of content containers(such as URL addresses) to the content nodes that are classified withinthe dimensional concept taxonomy. Container edits are also changes tothe descriptions of the content nodes within the dimensional concepttaxonomy.

Taxonomy edits are context changes to the position of the nodes in thedimensional concept taxonomy. These changes include the addition of newnodes into the structure and the repositioning of existing nodes. Thisdimensional concept taxonomy information is fed back into the system aschanges to the morpheme relationships that are associated with theconcepts that are affected by the user interactions.

With taxonomy edits, new relationships between concepts in the taxonomymay be created. These concept relationships are constructed through theuser interactions. Since these concepts are based on morphemes, newconcept relationships are associated with new sets of morphemerelationships. This dimensional concept taxonomy information is fed backinto the system to recalculate these implied morpheme relationships.

User interactions may also be provided at more elemental levels ofabstraction, such as keywords and morphemes.

FIG. 27 illustrates the outliner user interface. It shows devices tochange the location of nodes 2702 in the structure 2704 and to edit thecontainers and concept definition assignments at each node 2706.

A view of the dimensional concept taxonomy is presented to the userthrough the user interface described above. It is assumed, for thepurposes of illustration, that after reviewing the classification, theuser wishes to reorganize it.

In the preferred embodiment, using a client-side control, the user isable to move nodes in the hierarchy to reorganize the dimensionalconcept taxonomy. In so doing, the user would establish new parent-childrelationships between nodes.

As the location of the node is edited, it will make relevant a new setof relationships between the underlying morphemes. This in turn mayrequire a recalculation to determine the new set of inferred dimensionalconcept relationships. These changes are queued to calculate the newmorpheme relationships inferred by the concept relationships.

The changes may be stored as exceptions to a shared dimensional concepttaxonomy (hereinafter a community concept taxonomy) for the personalizedneeds of the user (see below for more details on personalization).

Those skilled in the art will appreciate that there are many suchcontrols and alternate technologies available to facilitate thisinteractivity.

FIG. 28 illustrates the preferred embodiment of the process of containeredits. Container edits are changes to the concept definitions and theunderlying morphemes that describe each content node. With thesechanges, users alter the underlying concept definition of a contentnode. In so doing, they alter the morphemes that are mapped to theconcept definitions at these content nodes.

The user interactions construct the concept definition assigned to thecontent node, expressed as a collection of keywords. In thisconstruction, the user interacts with the system's morpheme lexicon anddomain data store. Any new keywords that are created here are sent tothe system's morpheme extraction process, as described above.

In this example, a document 2801 is the active container. In the userinterface, the set of keywords 2802 that describe the content ispresented to the user along with the document. (The relative position ofthis node in the dimensional concept taxonomy is not shown here tosimplify the example.)

In the example, as the user reviews the content, the user determinesthat the keywords associated with the page are not optimal. New keywordsare selected by the user to replace the set that loaded with the page2803. The user updates the list of keywords 2804 as the new conceptdefinition associated with the document.

These changes are then passed to the domain data store 706. The datastore may be searched to identify all keywords registered in the system.

In this example, the list includes all keywords identified by the user,with the exception of “dog”. As a result, “dog” will be processed as animplicit keyword that modifies the explicit keywords that are registeredin the system 2806.

The implicit keywords will be analyzed in full when the domain isreviewed by the centralized transformation engine. It will then bereplaced by an explicit keyword (either as an existing keyword or a newkeyword) and associated with one or more morphemes.

1.1.4.3 Complex-Adaptive Processing

FIG. 29 illustrates the method for processing user interactions in acomplex-adaptive system. It builds upon the dimensional concept taxonomyprocess described above N. User interactions establish a series offeedback loops in the system. The adaptive process of refinement to thecomplex dimensional structures is accomplished through the feedbackloops initiated by end-users.

Therefore, we may summarize the methods of the complex-adaptive processas follows:

Provide dimensional concept taxonomy as an environment for userinteractions 212 a. Once a dimensional concept taxonomy 210 has beenpresented to users, it becomes an environment for revising existingdata, as well as a source for new data (dimensional concept taxonomyinformation). The input data 804 a comprised of the edits to existingdata and the input of new data by users. It also provides for evolvingand adapting the classifications to dynamic domains.

User interactions may comprise a feedback loop back in the system O.Unique identifiers in the data elements in the dimensional concepttaxonomy information are uniquely identified using a notation systembased on the morpheme elements stored in the centralized system. Thus,each data element in the dimensional concept taxonomies produced by thesystem is identified in a way that can be merged back into thecentralized (shared) morpheme lexicon.

Therefore, when users manipulate those elements, the contingent effectson the related morpheme elements may be tracked. These changes reflectnew explicit data in the system, to refine any of the inferred dataautomatically generated by the system. In other words, what wasoriginally inferred by the system may be reinforced or rejected by theexplicit interactions of the end-users.

User interactions may comprise both new data sources and revisions toknown data sources. Manipulations to known elements are translated backto their morpheme antecedents. Any data elements that are not recognizedby the system represent new data. However, since the changes are made inthe context of the existing dimensional concept taxonomy produced by thesystem, this new data may be placed in the context of known data. Thus,any new data elements added by users are provided in the context of theknown elements. The relationships between the known and the unknowngreatly extend the amount of dimensional concept taxonomy informationthat may be inferred from the users' interactions.

A “shortcut” feedback loop 212 c in the system provides a real-timeinteractive environment for end-users. The taxonomy and container edits2902 initiated by the user are queued in the system and formallyprocessed as system resources become available. Users, however,sometimes require (or prefer) real-time feedback to their changes to thedimensional concept taxonomy. The time required to process the changesthrough the system's formal feedback loops may delay this real-timefeedback to the user. As a result, the preferred embodiment of thesystem provides a shortcut feedback loop.

This shortcut feedback loop begins by processing user edits against thedomain data store 706 as it exists at that time. Since the users'changes may include dimensional concept taxonomy information that doesnot presently exist in the domain data store, the system must use aprocess that approximates the effect of the changes.

The rules for creating implicit relationships 212 b (described above)are applied to new data as a short-term surrogate for full processing.This approach allows users to immediately insert and interact with thenew data.

As opposed to the dimensional concept relationships calculated throughthe system's formal processes, this approximation process uses thepresence of morphemes unknown to the system in sets of known morphemesto qualify and adjust the dimensional concept relationships of the knownmorphemes in the set. These adjusted relationships are described as“implicit relationships” 216, described in greater detail above.

For new data elements, short-term concept definitions are assigned basedon implicit relationships (described above) to facilitate real-timeprocessing of the interactions. At the completion of the next fullprocessing cycle for the domain, the short-term implied conceptdefinitions are replaced with the complete concept definitions devisedby the system.

Those skilled in the art will appreciate that there are many algorithmsthat may be used to approximate the influence of unknown morphemes onthe relationships of known morphemes in the system.

1.1.4.4 Personalization

FIG. 30 illustrates an alternate embodiment of the invention whichprovides for features of personalization, wherein personalized versionsof the dimensional concept taxonomy may be maintained for eachindividual user of the domain.

Preferably, to personalize the community concept taxonomy 210 e, alongwith a personalized concept taxonomy 210 f for each individual user. Thefirst time an end-user interacts with the system, each end-user will beengaging the community concept taxonomy 210 e. Following interactionswill engage the user's personalized view of the taxonomy 210 f.

Data structures are “personalized” by collating a unique representationof the data structure in response to user interactions 212 arepresenting the preferences of each end user. The results of the editsare stored as the personalized data from the user interactions 3004. Inone embodiment, these edits are stored as “exceptions” to the communityconcept taxonomy 210 e. When the personal concept taxonomy 210 f isprocessed, the system substitutes any changes it finds in the users'exceptions table.

The elements illustrated identify the collaborators in the system'scomplex-adaptive processes. It provides a means to associate uniqueidentifiers with each user and store their interactions.

In the preferred embodiment, the system assigns unique identifiers toeach user that interacts with the dimensional concept taxonomy 210 ethrough the presentation layer. These identifiers may be considered asmorphemes. Every user is assigned a globally unique identifier (GUID),preferably a 128-bit integer (16 bytes) that can be used across allcomputers and networks. The user GUID exists as a morpheme in thesystem.

Like any other morpheme in the system, the user identifiers may beregistered in the morpheme hierarchy (explicit morphemes) or unknown tothe system (implicit morphemes).

The distinction between the two types of identifiers is akin to thedistinction between registered and anonymous visitors, in terms that arewell known in the art. The various ways that may be used to generate andassociate identifiers (or “trackers”) with users are also well known inthe art, and will not be discussed herein.

When a user interacts with the system (for example, by editing a contentcontainer), the system adds that user's identifier to the set ofmorphemes that describe the concept definition. The system may also addone or more morphemes that are associated with the various types ofinteractivity the system supports. For example, the user “Bob” may wishto edit the container with the concept definition, “recording, studio”to include a geographic reference. The system may thus create thefollowing concept definition record for that container, specific to Bob:{Bob, Washington, (recording, studio)}.

With this dimensional concept taxonomy information, the system couldpresent the container in a manner specific to the user, Bob, by applyingthe same rules of explicit and implicit relationship calculations in theenhanced method of faceted classification described above. The containermay appear on the personal Web page for Bob. In his personal concepttaxonomy, the page would be related to resources in Washington.

The dimensional concept taxonomy information would also be availableglobally to other users, as well, subject to the statistical analysesand hurdle rates established by the administrators as a negativefeedback mechanism. For example, if enough users identified the locationof Washington with the recording studio, it would eventually bepresented to all users as a valid relationship.

This type of modification to the concept definitions associated with thecontent container essentially adds new layers of dimensionality to thedimensional concept taxonomy information representing the various layersof user interactivity. It provides a versatile mechanism forpersonalization using the existing constructive processes applied toother forms of information and content.

As is well known in the art, there are many technologies andarchitectures available for adding personalization and customizedpresentation layers. The method discussed herein makes use of thesystem's core structural logic to organize collaborators. It essentiallytreats user interactions as just another type of informational element,illustrating the flexibility and extensibility of the system. It doesnot, however, limit the scope of the invention in the various methodsfor adding customization and personalization to the system.

1.1.4.5 Machine-Based Complex-Adaptive System

FIG. 31 illustrates an alternate embodiment that provides amachine-based means for providing a complex-adaptive system, wherein thedimensional concept relationships that comprise the dimensional concepttaxonomy 210 are returned directly back into the transformation engineprocesses 3102 as system input data 804 b.

Note that there is an important distinction between the original conceptrelationships derived from the source data structure and the dimensionalconcept relationships that emerge from the processes of the system buildengine. The former are explicit in the source data structure; the latterare derived from (or emerge through) the constructive methods appliedagainst elemental constructs within the morpheme lexicon. Thus, themachine-based approach, like the complex-adaptive system based on userinteractions, provides a means for introducing variation in the systemoperations 800 through the synthesis of (complex) dimensional conceptrelationships from elemental constructs, and then selecting from thatvariation in the source structure analytics component.

Under this machine-based mode of operation, the selection requirementfor the complex-adaptive system is borne by the source structureanalytics component (described above and illustrated in FIG. 10).Specifically, dimensional concept relationships are selected based onthe identification of circular relationships 1002 and the various modesand parameters that may be used to resolve these circular relationships.As is well known in the art, there are many alternate means, selectioncriteria, and analytical tools to provide for a machine-basedcomplex-adaptive system.

Dimensional concept relationships that contravene the assumptions ofhierarchy, identified in the aggregate through the presence of circularrelationships, may be pruned from the data set 1004. This pruned dataset is reassembled 1006 into an input concept taxonomy 1008, from whichthe operations 800 may derive a new set of elemental constructs throughthe remaining operations of the analysis engine.

This type of machine-based complex-adaptive system may be used inconjunction with other complex-adaptive systems, such as the system 212based on user interactions, described above with reference to FIGS. 8and 29. For example, the machine-based complex-adaptive system of FIG.31 may be used to refine the dimensional concept taxonomy throughseveral iterations of the process. Thereafter, the resultant dimensionalconcept taxonomy may be introduced to users in the user-basedcomplex-adaptive system for further refinement and evolution.

1.2 System Architecture

As emphasized throughout this description of the system architecture,there is much variability in the methods and technologies forengineering the many embodiments of this invention, including datastores. The many applications of the invention may be exposed and variedthrough the many forms of architectural engineering that are well knownin the art.

1.2.1 Architecture Components

FIG. 32 illustrates the preferred embodiment of the computingenvironment for the invention.

In the preferred embodiment, the present invention is implemented as acomputer software program operating under a four-tier architecture.Server application software and databases execute on both centralizedcomputers and distributed, decentralized systems. The Internet is usedto as the network to communicate between the centralized servers and thevarious computing devices and distributed systems that interact with it.

The variability and methods for establishing this type of computingenvironment are well known in the art. As such, no further discussion ofthe computing environment is contained herein. What is common to allapplicable environments is that the user accesses a public or privatenetwork, such as the Internet or a company's intranet, through his orher computer or computing device, thereby accessing the computersoftware that embodies the invention.

Each tier is responsible for providing a service. Tiers one 3202 and two3204 operate under a model of centralized processing. Tiers three 3206and four 3208 operate under a model of distributed processing.

This four-tier model realizes the decentralization of private domaindata from the shared centralized data that the system uses to analyzedomains. This delineation between shared and private data is discussedabove, illustrated in FIG. 7.

At the first tier, a centralized data store represents the various dataand content sources that are managed by the system. In the preferredembodiment, a database server 3210 provides data services, and the meansof accessing and maintaining the data.

Although the distributed content is described here as being containedwithin a “database”, data can be stored in a plurality of linkedphysical locations or data sources.

Metadata may also be decentralized and stored externally from the systemdatabase. For example, HTML code fragments that contain metadata thatmay be acted upon by the system. Elements from the external schema maybe mapped to the elements used in the schema of the present system.Other formats for presenting metadata are well known in the art. Theinformational landscape may thus provide a wealth of distributed contentsources and a means for end-users to manage the information in adecentralized way.

The techniques and methods for managing data across a plurality oflinked physical locations or data sources is well known in the art, andwill not be further exhaustively discussed herein.

XML data feeds and application programming interfaces (API) 3212 areused to connect the data store 3210 to the application server 3214.

Again, those skilled in the art understand that the XML may conform to abroad range of proprietary and open schema. A range of data interchangetechnologies provide the infrastructure to incorporate a variety ofdistributed content formats into the system. This and all followingdiscussion of the connectors used in the preferred embodiment do notlimit the scope of the present invention.

At the second tier 3204, an application that resides on a centralizedserver 3214 contains the core programming logic for the invention. Theapplication server provides the core programming logic and processingrules of the invention, along with connectivity to the database server.This programming logic is described in detail above, illustrated inFIGS. 8-25.

In the preferred embodiment, the structural information processed by theapplication server is output as XML 3216. XML is used to connectexternal data stores and Web sites with the application server.

Again, XML 3216 is used to communicate this interactivity back to theapplication server for further processing in an ongoing process ofoptimization and refinement.

At the third tier, a distributed data store 3218 is used to store domaindata. In the preferred embodiment, this data is stored in the form ofXML files on a web server. There are many alternate modes of storing thedomain data such as external databases. The distributed data store isused to distribute the output data to presentation devices of end users.

In the preferred embodiment, the output data is distributed as XML datafeeds, rendered using XSL transformation files (XSLT) 3220. Thesetechnologies render the output data through a presentation layer at thefourth tier.

The presentation layer may be any decentralized web sites, clientsoftware, or other media that presents the taxonomies in a form that maybe utilized by humans or machines. The presentation layer represents theoutward manifestation of the taxonomies and the environments throughwhich end-users interact with the taxonomies. In the preferredembodiment, the data is rendered as a web site and displayed in abrowser.

This structured information provides the platform for user collaborationand input. Those skilled in the art will appreciate that XML and XSLTmay be used to render information across a diverse range of computingplatforms and media. This flexibility allows the system to be used as aprocess within a broad range of information processing tasks.

For example, morphemes are expressed using the keywords in the datafeed. By including the morpheme references in the data feed, the systemprovides for additional processing on the presentation layer in responseto specific morphemic identifiers. An application of this flexibility isdescribed above in the discussion of personalization (FIG. 30).

Using web-based forms and controls 3224, users may add and modifyinformation in the system. This input is then returned to thecentralized processing systems via the distributed data store as XMLdata feeds 3226 and 3216.

Additionally, open XML formats such as RSS may also be incorporated fromthe Internet as inputs to the system.

Modifications to the structural information are processed by theapplication server 3214. Shared morpheme data from this processing isreturned via XML and API connectors 3212 and stored in the centralizeddata store 3210.

Within the broad field of system architecture, there are many possibledesigns, modes, and products, which are well known. These includecentralized, decentralized, and open access models of systemarchitecture. The technical workings of these implementations and thevarious alternatives that are covered by this invention will not befurther discussed herein.

1.2.2 Database Schema

FIG. 33 provides a simplified overview of the core data structureswithin the system in the preferred embodiment of the invention. Thissimplified schema illustrates the manner in which data is transformedthrough the system's application programming logic. It also illustrateshow the morpheme data is deconstructed and stored.

The data architecture of the system was designed to centralize themorpheme lexicon, while providing temporary data stores for processingdomain-specific entities.

Note that domain data flows through the system; preferably, it is notstored in the system. The tables that map to the domain entities aretemporary data stores, which are then transformed to the output data andthe data store for the domain. The domain data store may be stored alongwith the other centralized assets or (preferably) distributed to storageresources maintained by the domain owner.

In the preferred embodiment, the application and database servers(described above and illustrated in FIG. 32) primarily manipulate data.The data is organized within three broad areas of data abstraction inthe system:

The entity abstraction layer 3302, where entities are the main buildingblocks of knowledge representation in the system. Entities are comprisedof: morphemes 3304, keywords 3306, concepts 3308, content nodes 3310,and content containers 3312 (represented by URLs).

The relationship layer of abstraction 3314, where entity definitions arerepresented by the relationships between the various entities used inthe system. Entity relationships are comprised of morpheme relationships3316, concept relationships 3318, keyword-morpheme relationships 3320,concept-keyword relationships 3322, node-concept relationships 3324, andnode-content container (URL) relationships 3326.

The label abstraction layer 3328 is where the terms used to describeentities are separated from the structural definitions of the entitiesthemselves. Labels 3330 are comprised of morpheme labels 3332, keywordlabels 3334, concept labels 3336, and node labels 3338. Labels may beshared across the various entities. Alternatively, labels may besegmented by entity type.

Note that this simplified schema in no way limits the database schemaused in the preferred embodiment. Issues of system performance, storage,and optimization figure prominently. Those skilled in the art know thatthere are many ways to design a database system that reflects the designelements described herein. As such, the various methods, technologies,and designs that may be used as embodiments in the present will not bediscussed further herein.

1.2.3 XML Schema and Client-Side Transformations

Faceted output data is encoded as XML and rendered by XSLT. The facetedoutput can be reorganized and represented in many different ways (forexample, refer to the published XFML schema). Alternate outputs forrepresenting hierarchies are available.

XSL transformation code (XSLT) is used in the preferred embodiment topresent the presentation layer (in this case, a Web site). Allinformation elements managed by the system (including distributedcontent if it is channeled through the system) may be rendered by XSLT.

Client-side processing is the process of the preferred embodiment toconnect data feeds to the presentation layer of the system. These typesof connectors are used to output information from the application serverto the various media that use the structural information. XML data fromthe application server may be processed through XSLT for presentation ona web page.

Those skilled in the art will appreciate the current and futurefunctionality that XML technologies and similar presentationtechnologies will provide in the service of this invention. In additionto basic publishing and data presentation, XSLT and similar technologiesprovide a range of programmatic opportunities. Complex informationstructures such as those created by the system provide actionableinformation, much like data models. Software programs and agents can actupon the information on the presentation layer, to providesophistication interactivity and automation. As such, the scope ofinvention provided by the core structural advantages of the system willextend far beyond the simple publishing.

Those skilled in the art will appreciate the variability that ispossible for architecting these XML and XSLT locations. For example, thefiles may be stored locally on the computers of end-users or generatedusing web services. ASP code (or similar technology) may be used toinsert the information managed by our system on distributed presentationlayers (such as the web pages of third-party publishers or softwareclients).

As another example, an XML data feed containing the core structuralinformation from the system may be combined with the distributed contentthat the system organizes. Those skilled in the art will appreciate theopportunities to decouple these two types of data into separate datafeeds.

These and other architectural opportunities for storing and distributingthese presentation files and data feeds are well known in the art, andwill therefore not be discussed further herein.

Any element in a claim that does not explicitly state “means for”performing a specified function, or “step for” performing a specificfunction, is not to be interpreted as a “means” or “step” clause asspecified in 35 USC §112, paragraph 6.

It will be appreciated by those skilled in the art that the inventioncan take many forms, and that such forms are within the scope of theinvention as claimed. Therefore, the spirit and scope of the appendedclaims should not be limited to the descriptions of the preferredversions contained herein.

1. In a computer system comprising at least one hardware processor andat least one tangible storage medium, a method of identifying elementalfacet attribute relationships in a domain of information comprising aplurality of concepts, wherein the method comprises: identifying aplurality of concept relationships between pairs of concepts in theplurality of concepts; identifying at least one facet attribute in eachof at least some of the pairs of concepts; based, at least in part, onthe identified plurality of concept relationships, identifying a set ofcandidate facet attribute relationships between the facet attributes inthe at least some of the pairs of concepts; and determining whether toinclude a candidate facet attribute relationship from the set ofcandidate facet attribute relationships in a modified set of candidatefacet attribute relationships based, at least in part, on thepervasiveness of the candidate facet attribute relationship.
 2. Themethod of claim 1, wherein the pervasiveness of the candidate facetattribute relationship is measured as the likelihood that the facetattribute relationship holds true in all concept relationships in whichit presents.
 3. The method of claim 2, wherein the likelihood that thecandidate facet attribute relationship holds true in all conceptrelationships in which it presents is determined based on a frequencywith which the facet attributes related by the candidate facet attributerelationship present together in the domain of information.
 4. Themethod of claim 2, wherein the plurality of concept relationshipscomprises a first concept relationship between a first of the pluralityof concepts and a second of the plurality of concepts, and wherein theact of identifying at least one facet attribute in at least some of thepairs of concepts further comprises: identifying first and second facetattributes in the first of the plurality of concepts and identifyingthird and fourth attributes in the second of the plurality of concepts.5. The method of claim 4, wherein the act of identifying a set ofcandidate facet attribute relationships between the facet attributes inthe at least some of the pairs of concepts further comprises:identifying a first facet attribute relationship between the first andthird facet attributes; identifying a second facet attributerelationship between the first and fourth facet attributes; identifyinga third facet attribute relationship between the second and third facetattributes; and identifying a fourth facet attribute relationshipbetween the second and fourth facet attributes.
 6. The method of claim5, wherein the act of determining whether to include a candidate facetattribute relationship in a modified set of candidate facet attributerelationships based, at least in part, on the pervasiveness of the facetattribute relationship further comprises: determining whether to includethe first facet attribute relationship between the first and third facetattributes in the modified set of candidate facet attributerelationships based on a prevalence of a relationship between the firstand third facet attributes in the identified plurality of conceptrelationships.
 7. The method of claim 1, wherein the act of identifyingat least one facet attribute in each of at least some of the pairs ofconcepts further comprises: identifying a plurality of keywords in theat least some of the pairs of concepts; and identifying the plurality offacet attributes in the plurality of keywords.
 8. The method of claim 7,wherein the act of identifying the plurality of facet attributes in theplurality of keywords further comprises: defining at least one patternfor use as criteria for identifying facet attribute candidates; applyingthe at least one pattern against the plurality of keywords; and based onthe application of the at least one pattern, identifying a candidate setof facet attributes.
 9. The method of claim 8, further comprising:comparing the candidate facet attributes in the candidate set of facetattributes to identify inconsistent candidate facet attributes; andremoving inconsistent candidate facet attributes from the candidate setof facet attributes.
 10. At least one computer readable medium encodedwith computer instructions that, when executed in a computer systemperform a method of identifying elemental facet attribute relationshipsin a domain of information comprising a plurality of concepts, whereinthe method comprises: identifying a plurality of concept relationshipsbetween pairs of concepts in the plurality of concepts; identifying atleast one facet attribute in each of at least some of the pairs ofconcepts; based, at least in part, on the identified plurality ofconcept relationships, identifying a set of candidate facet attributerelationships between the facet attributes in the at least some of thepairs of concepts; and determining whether to include a candidate facetattribute relationship from the set of candidate facet attributerelationships in a modified set of candidate facet attributerelationships based, at least in part, on the pervasiveness of thecandidate facet attribute relationship.
 11. The at least one computerreadable medium of claim 10, wherein the pervasiveness of the candidatefacet attribute relationship is measured as the likelihood that thefacet attribute relationship holds true in all concept relationships inwhich it presents.
 12. The at least one computer readable medium ofclaim 11, wherein the likelihood that the candidate facet attributerelationship holds true in all concept relationships in which itpresents is determined based on a frequency with which the facetattributes related by the candidate facet attribute relationship presenttogether in the domain of information.
 13. The at least one computerreadable medium of claim 11, wherein the plurality of conceptrelationships comprises a first concept relationship between a first ofthe plurality of concepts and a second of the plurality of concepts, andwherein the act of identifying at least one facet attribute in at leastsome of the pairs of concepts further comprises: identifying first andsecond facet attributes in the first of the plurality of concepts andidentifying third and fourth attributes in the second of the pluralityof concepts.
 14. The at least one computer readable medium of claim 13,wherein the act of identifying a set of candidate facet attributerelationships between the facet attributes in the at least some of thepairs of concepts further comprises: identifying a first facet attributerelationship between the first and third facet attributes; identifying asecond facet attribute relationship between the first and fourth facetattributes; identifying a third facet attribute relationship between thesecond and third facet attributes; and identifying a fourth facetattribute relationship between the second and fourth facet attributes.15. The at least one computer readable medium of claim 14, wherein theact of determining whether to include a candidate facet attributerelationship in a modified set of candidate facet attributerelationships based, at least in part, on the pervasiveness of the facetattribute relationship further comprises: determining whether to includethe first facet attribute relationship between the first and third facetattributes in the modified set of candidate facet attributerelationships based on a prevalence of a relationship between the firstand third facet attributes in the identified plurality of conceptrelationships.
 16. The at least one computer readable medium of claim10, wherein the act of identifying at least one facet attribute in eachof at least some of the pairs of concepts further comprises: identifyinga plurality of keywords in the at least some of the pairs of concepts;and identifying the plurality of facet attributes in the plurality ofkeywords.
 17. The at least one computer readable medium of claim 16,wherein the act of identifying the plurality of facet attributes in theplurality of keywords further comprises: defining at least one patternfor use as criteria for identifying facet attribute candidates; applyingthe at least one pattern against the plurality of keywords; and based onthe application of the at least one pattern, identifying a candidate setof facet attributes.
 18. The at least one computer readable medium ofclaim 17, wherein the method further comprises: comparing the candidatefacet attributes in the candidate set of facet attributes to identifyinconsistent candidate facet attributes; and removing inconsistentcandidate facet attributes from the candidate set of facet attributes.19. A computer comprising: at least one tangible memory that storesprocessor-executable instructions for performing a method identifyingelemental facet attribute relationships in a domain of informationcomprising a plurality of concepts; and at least one hardware processor,coupled to the at least one tangible memory, configured to execute theprocessor-executable instructions to: identify a plurality of conceptrelationships between pairs of concepts in the plurality of concepts;identify at least one facet attribute in each of at least some of thepairs of concepts; based, at least in part, on the identified pluralityof concept relationships, identify a set of candidate facet attributerelationships between the facet attributes in the at least some of thepairs of concepts; and determine whether to include a candidate facetattribute relationship from the set of candidate facet attributerelationships in a modified set of candidate facet attributerelationships based, at least in part, on the pervasiveness of thecandidate facet attribute relationship.
 20. The computer of claim 19,wherein the pervasiveness of the candidate facet attribute relationshipis measured as the likelihood that the facet attribute relationshipholds true in all concept relationships in which it presents.
 21. Thecomputer of claim 20, wherein the likelihood that the candidate facetattribute relationship holds true in all concept relationships in whichit presents is determined based on a frequency with which the facetattributes related by the candidate facet attribute relationship presenttogether in the domain of information.
 22. The computer of claim 20,wherein the plurality of concept relationships comprises a first conceptrelationship between a first of the plurality of concepts and a secondof the plurality of concepts, and wherein the at least one hardwareprocessor executes the processor executable instructions to identify theat least one facet attribute in the at least some of the pairs ofconcepts by identifying first and second facet attributes in the firstof the plurality of concepts and identifying third and fourth attributesin the second of the plurality of concepts.
 23. The computer of 22,wherein the at least one hardware processor executes the processorexecutable instructions to identify a set of candidate facet attributerelationships between the facet attributes in the at least some of thepairs of concepts by: identifying a first facet attribute relationshipbetween the first and third facet attributes; identifying a second facetattribute relationship between the first and fourth facet attributes;identifying a third facet attribute relationship between the second andthird facet attributes; and identifying a fourth facet attributerelationship between the second and fourth facet attributes.
 24. Thecomputer claim 23, wherein the at least one hardware processor executesthe processor executable instructions to determine whether to include acandidate facet attribute relationship in a modified set of candidatefacet attribute relationships based, at least in part, on thepervasiveness of the facet attribute relationship by: determiningwhether to include the first facet attribute relationship between thefirst and third facet attributes in the modified set of candidate facetattribute relationships based on a prevalence of a relationship betweenthe first and third facet attributes in the identified plurality ofconcept relationships.
 25. The computer of claim 19, wherein the atleast one hardware processor executes the processor executableinstructions to identify at least one facet attribute in each of atleast some of the pairs of concepts by: identifying a plurality ofkeywords in the at least some of the pairs of concepts; and identifyingthe plurality of facet attributes in the plurality of keywords.
 26. Thecomputer of claim 25, wherein the at least one hardware processorexecutes the processor executable instructions to identify the pluralityof facet attributes in the plurality of keywords by: defining at leastone pattern for use as criteria for identifying facet attributecandidates; applying the at least one pattern against the plurality ofkeywords; and based on the application of the at least one pattern,identifying a candidate set of facet attributes.
 27. The computer ofclaim 26, wherein the at least one hardware processor executes theprocessor executable instructions to: comparing the candidate facetattributes in the candidate set of facet attributes to identifyinconsistent candidate facet attributes; and removing inconsistentcandidate facet attributes from the candidate set of facet attributes.28. The method of claim 1, wherein the method is a method foridentifying elemental facet attribute relationships by stripping awaynoise.
 29. The at least one computer readable medium of claim 10,wherein the computer instructions, when executed in the computer system,perform a method of identifying elemental facet attribute relationshipsby stripping away noise.
 30. The computer of claim 19, wherein theprocessor-executable instructions are instructions for performing amethod identifying elemental facet attribute relationships by strippingaway noise.